{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习练习-支持向量机\n",
    "代码更新地址：https://github.com/fengdu78/WZU-machine-learning-course\n",
    "\n",
    "\n",
    "代码修改并注释：黄海广，haiguang2000@wzu.edu.cn "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本练习中，我们将使用支持向量机（SVM）来构建垃圾邮件分类器。 我们将从一些简单的2D数据集开始使用SVM来查看它们的工作原理。 然后，我们将对一组原始电子邮件进行一些预处理工作，并使用SVM在处理的电子邮件上构建分类器，以确定它们是否为垃圾邮件。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们要做的第一件事是看一个简单的二维数据集，看看线性SVM如何对数据集进行不同的C值（类似于线性/逻辑回归中的正则化项）。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将其用散点图表示，其中类标签由符号表示（+表示正类，o表示负类）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv('data/svmdata1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.9643</td>\n",
       "      <td>4.5957</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.2753</td>\n",
       "      <td>3.8589</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.9781</td>\n",
       "      <td>4.5651</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.9320</td>\n",
       "      <td>3.5519</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5772</td>\n",
       "      <td>2.8560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X1      X2  y\n",
       "0  1.9643  4.5957  1\n",
       "1  2.2753  3.8589  1\n",
       "2  2.9781  4.5651  1\n",
       "3  2.9320  3.5519  1\n",
       "4  3.5772  2.8560  1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive = data1[data1['y'].isin([1])]\n",
    "negative = data1[data1['y'].isin([0])]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax.scatter(positive['X1'], positive['X2'], s=50, marker='x', label='Positive')\n",
    "ax.scatter(negative['X1'], negative['X2'], s=50, marker='o', label='Negative')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请注意，还有一个异常的正例在其他样本之外。\n",
    "这些类仍然是线性分离的，但它非常紧凑。 我们要训练线性支持向量机来学习类边界。 在这个练习中，我们没有从头开始执行SVM的任务，所以我要用scikit-learn。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1, loss='hinge')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "svc = svm.LinearSVC(C=1, loss='hinge', max_iter=1000)\n",
    "svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，我们使用 C=1 看下结果如何。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9803921568627451"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(data1[['X1', 'X2']], data1['y'])\n",
    "svc.score(data1[['X1', 'X2']], data1['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其次，让我们看看如果C的值越大，会发生什么"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9411764705882353"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc2 = svm.LinearSVC(C=100, loss='hinge', max_iter=1000)\n",
    "svc2.fit(data1[['X1', 'X2']], data1['y'])\n",
    "svc2.score(data1[['X1', 'X2']], data1['y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "这次我们得到了训练数据的完美分类，但是通过增加C的值，我们创建了一个不再适合数据的决策边界。 我们可以通过查看每个类别预测的置信水平来看出这一点，这是该点与超平面距离的函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['SVM 1 Confidence'] = svc.decision_function(data1[['X1', 'X2']])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax.scatter(data1['X1'],\n",
    "           data1['X2'],\n",
    "           s=50,\n",
    "           c=data1['SVM 1 Confidence'],\n",
    "           cmap='seismic')\n",
    "ax.set_title('SVM (C=1) Decision Confidence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['SVM 2 Confidence'] = svc2.decision_function(data1[['X1', 'X2']])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.scatter(data1['X1'], data1['X2'], s=50, c=data1['SVM 2 Confidence'], cmap='seismic')\n",
    "ax.set_title('SVM (C=100) Decision Confidence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看看靠近边界的点的颜色，区别是有点微妙。 如果您在练习文本中，则会出现绘图，其中决策边界在图上显示为一条线，有助于使差异更清晰。\n",
    "\n",
    "现在我们将从线性SVM转移到能够使用内核进行非线性分类的SVM。 我们首先负责实现一个高斯核函数。 虽然scikit-learn具有内置的高斯内核，但为了实现更清楚，我们将从头开始实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gaussian_kernel(x1, x2, sigma):\n",
    "    return np.exp(-(np.sum((x1 - x2)**2) / (2 * (sigma**2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32465246735834974"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = np.array([1.0, 2.0, 1.0])\n",
    "x2 = np.array([0.0, 4.0, -1.0])\n",
    "sigma = 2\n",
    "\n",
    "gaussian_kernel(x1, x2, sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该结果与练习中的预期值相符。 接下来，我们将检查另一个数据集，这次用非线性决策边界。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.read_csv('data/svmdata2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.603070</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.093318</td>\n",
       "      <td>0.649854</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.097926</td>\n",
       "      <td>0.705409</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.155530</td>\n",
       "      <td>0.784357</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.210829</td>\n",
       "      <td>0.866228</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2  y\n",
       "0  0.107143  0.603070  1\n",
       "1  0.093318  0.649854  1\n",
       "2  0.097926  0.705409  1\n",
       "3  0.155530  0.784357  1\n",
       "4  0.210829  0.866228  1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive = data2[data2['y'].isin([1])]\n",
    "negative = data2[data2['y'].isin([0])]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax.scatter(positive['X1'], positive['X2'], s=30, marker='x', label='Positive')\n",
    "ax.scatter(negative['X1'], negative['X2'], s=30, marker='o', label='Negative')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于该数据集，我们将使用内置的RBF内核构建支持向量机分类器，并检查其对训练数据的准确性。 为了可视化决策边界，这一次我们将根据实例具有负类标签的预测概率来对点做阴影。 从结果可以看出，它们大部分是正确的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, gamma=10, probability=True)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = svm.SVC(C=100, gamma=10, probability=True)\n",
    "svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9698725376593279"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(data2[['X1', 'X2']], data2['y'])\n",
    "svc.score(data2[['X1', 'X2']], data2['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2['Probability'] = svc.predict_proba(data2[['X1', 'X2']])[:, 0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax.scatter(data2['X1'], data2['X2'], s=30, c=data2['Probability'], cmap='Reds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于第三个数据集，我们给出了训练和验证集，并且基于验证集性能为SVM模型找到最优超参数。 虽然我们可以使用scikit-learn的内置网格搜索来做到这一点，但是本着遵循练习的目的，我们将从头开始实现一个简单的网格搜索。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3=pd.read_csv('data/svmdata3.csv')\n",
    "data3val=pd.read_csv('data/svmdata3val.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data3[['X1','X2']]\n",
    "Xval = data3val[['X1','X2']]\n",
    "y = data3['y'].ravel()\n",
    "yval = data3val['yval'].ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.965, {'C': 0.3, 'gamma': 100})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_values = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100]\n",
    "gamma_values = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100]\n",
    "\n",
    "best_score = 0\n",
    "best_params = {'C': None, 'gamma': None}\n",
    "\n",
    "for C in C_values:\n",
    "    for gamma in gamma_values:\n",
    "        svc = svm.SVC(C=C, gamma=gamma)\n",
    "        svc.fit(X, y)\n",
    "        score = svc.score(Xval, yval)\n",
    "\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_params['C'] = C\n",
    "            best_params['gamma'] = gamma\n",
    "\n",
    "best_score, best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 大间隔分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, kernel='linear')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bad models\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5 * x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], \"bs\", label=\"Iris-Versicolor\")\n",
    "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], \"yo\", label=\"Iris-Setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], \"bs\")\n",
    "plt.plot(X[:, 0][y == 0], X[:, 1][y == 0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征缩放的敏感性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3EEII4WFCQkL44x//yIIFCzh69Cj//e9/iYiIICYmxmrzz3/+k379+vH++++TmppqY7RCiDySeAshhBAeLDg4mD/84Q/MnTuX9u3bW9tnzJjBsmXLePzxx2ncuDG9e/fm//7v/0hJSbEt1unTITzcsTpweLjjtRBViSTeQgghRCU0d+5cpk+fzujRo6lWrRpJSUk8/fTThIWF8fzzz7s9nunTYcIEOHAAtHb8OWGCJN+iapHEWwghhKiEatWqxd133018fDzHjx/nu+++wzAMAgIC6NjRWq+On3/+mXfeeYfdu3eXazwvvwyFF+XMyHBsF6KqUJXh4YuuXbvqtWvX2h2GEEKUmFJqnda6q91xuJP02fbKyMjAy8uLatWqAfDggw/y+eefA9CpUycMw8AwDG688cYyfV8vL8dId2FKORYqE8ITlLbPlhFvIYQQogoJCAiwkm6AmJgY7rnnHmrVqsXGjRuZNGkSrVq1on379nz88cdl9r5hYSXbLkRlJIm3EEIIUYUNHTqUadOmcezYMebMmcO4ceOoXbs2W7Zs4eDBg1a748ePs2nTpmsuU/jmmxAQUHBbQIBjuxBVhSTeQgghhMDf399aDfPo0aPMnz+fBx980No/ffp0OnbsyI033shLL73E+vXrS5SEjx0Ln34KzZo5ppc0a+Z4PXZseXwaISomSbyFEEIIUYCfnx9Dhw6lefPm1rbc3FxCQkLYtWsXb731Fl26dOH666/nueeeo7hz9seOhf37HXO69++XpFtUPZJ4CyGEEOKq/vznP5OWlsaSJUt49NFHadCgAfv27ePdd99l8uTJVrvc3Fxy5WlJIVySxFsIIYRbpaWlsXPnTrvDENfA29vbWg3z0KFD/PTTTzzxxBOMHz/earN48WLCwsJ48sknWb58OTk5OTZGLETFIuUEhRDCRlWxnKBSSgO0b9/eKl3Xpk0bu8MSZeS5557j3XfftV43aNCAMWPGYBgGffr0wcfHx8bohCid0vbZkngLIYSNPDnxVko9BowD2gP/01qPK8731atXT2dnZ3P27FlrW+vWra0kvH379iilyiNk4QZaa9auXYtpmsTGxrJv3z5r380338zKlSttjE6I0pE63kIIIeySCrwB/Lck3xQeHs6xY8dITExk/Pjx1K1bl23btjFlyhQ6duxIq1atePnll9mwYcM1l64T9lFK0a1bN9555x327NnDunXrePHFF7nhhhu49dZbrXaHDx/mgQceYN68eVy6dMnGiIVwHxnxFkIIG3nyiHcepdQbQJPijngX7rOzsrJYunQppmkSHx/PiRMnrH3Nmze3RsK7du0qI+EeTGvNpUuX8Pf3B+D999/n8ccfByAoKIioqChiYmIYPHiw1UaIikammiCJtxDCc1WVxFspNQGYABAWFtblwIEDLttlZ2fz008/WUn40aNHrX1hYWFWEn7zzTfj5SU3bT3Z7t27+eabb4iNjWXLli3W9lq1ahEdHc3nn38uF1qiwpHEG0m8hRCeq6ok3vkVt8/OyckhKSkJ0zSJi4sjNTXV2te4cWOio6OJiYmhZ8+ekoR7uO3btxMXF4dpmmzcuJGIiAjmzp0LOH4OZs2axdChQwkMDLQ5UlHVSeKNJN5CCM8liXfx5ObmsnLlSkzTxDRNUlJSrH0NGzYsUDXD29u7RMcWFcvu3bvJyMigQ4cOACxdupT+/ftTvXp1IiIiiImJYcSIEdSoUcPmSEVVJA9XCiGEqPS8vLzo2bMn//jHPzhw4ACrVq1i4sSJhIeHk5aWxgcffED//v1p1KgRDz/8MIsWLSI7O9vusMU1uOGGG6ykGxwj3j169ODChQvExcVx5513EhISwujRo5k+fbr8PQuPIiPeQghhI08e8VZK+QA+wKtAE+CPQLbW+oqZUFn22Vpr1q9fb5Wu27Nnj7UvODiYUaNGYRgGAwcOxNfXt0zeU9gjJSXFmo6SlJQEOOb979+/35oLfu7cORkJF+VKppogibcQwnN5eOL9Go6kO7/XtdavXen7yqvP1lqzadMmKwnfsWOHta9OnTpERUVhGAaDBg2Sqhke7vDhw8yYMQMfHx8efvhhAI4ePUqzZs3o378/hmEwatQogoODbY5UVDaSeCOJtxDCc3ly4n2t3NFna63ZunUrsbGxmKZJcnKyta9WrVqMHDkSwzAYOnQo1apVK9dYhHvMmjWLMWPGkJubCziWtx8wYICVhNevX9/mCEVlIIk3kngLITyXJN7usW3bNmuawq+//mptr1GjBpGRkRiGwfDhwwkICHBrXKJsHT9+nFmzZhEbG8uPP/5ITk4OAP7+/hw/fpyaNWvaHKHwdJJ4I4m3EFVCdjakpEBqKly6BH5+0KgRNG0KPj52R3fNJPF2v127dllJ+Lp166ztAQEBjBgxAsMwiIiIkLnCHu7kyZMkJCRgmiZKKebMmQM4KuRER0fTv39/xowZQ5MmTWyOVHgSSbyBLl266DVr1kgdVyEqI61hyxbYtcvx2jmCBUBe2bgWLaBdO/DAxTYk8bbXvn37rBKFq1evtrZXq1aN4cOHYxgGkZGR1KpVy8YoRWnl5uZaOcKKFSvo06ePta9nz54YhkF0dDRhYWF2hSg8hCTegFJKO//Ex8fH+qpTpw75V0fr378/hw8fLtDG29sbHx8f7r33Xh555BEANm7cyKRJkwq0y//117/+1ZorNm3aNLZs2eKy3XXXXYdhGABcunSJ6dOnF3jP/F+dO3emYcOGgOOhkUOHDrk8pq+vb4GOIT09HS8vL+uY3t7estKXqDy0hqQkOHasYMJdmLc31K8PvXp5XPItiXfFceDAAeLj4zFNk59//tna7ufnx9ChQzEMg5EjR1K7dm37ghSllp6ezpw5czBNk8TERC5evGjt6969OwkJCYSGhtoYoajIPDLxVkrdieNJ+DDgCDBOa71cKTUQ+MC5fZVzu+t1hQsez+WHqFu3LidPnrReh4eHU9QyxS+++CJ//etfAVi4cCFDhw4t8v327dtHeHg4ANHR0cTHx7tsN3jwYBYuXAjAqVOnrvh0dWxsrJWkv/3227z44osu29WpU4dTp05Zr6+77jr2799foI23tzfe3t4899xzTJkyBYBly5Zx9913F3kxkZCQQOPGjQGYPHkyK1ascNmuQ4cOvPDCCwCcP3+eF1988bILibzX0dHRtG7dGoD169ezevVql8esXr06I0aMsOJfuXIlWVlZLo8ZEhJCSEgIAJmZmZw9e9blMeXuRyWxebNjpPtKSXceb2/HyHf79uUfVxmSxLtiOnz4sJWEL1++nLzflb6+vgwePBjDMIiKiqJu3bo2RypK49y5cyQmJmKaJnPnzqV27dqkpKRYv0OmT59O9+7dadGihc2RioqitH222ydGKqUGA+8AdwCrgYbO7fWAeOBBYDYwBfgO6HG1Y3bp0oW1a9eSm5tLdna29ZVT6Jf1smXLyMzMLNAm76tRo0ZWu06dOpGQkGAdo3DbevXqWW3vvfdeunfv7vKYN9xwg9XO19eXcePGXdYm7/h5o90ADRo0KPKYhW93BgQEEBgYWOB4eV/5L6oyMjIKLLdcWP5ztXHjRn744QeX7U6dOmUl3hkZGfz73/8u8pht2rSxEu958+YxadIkl+2Cg4M5ceKE9fqOO+7g4MGDLtvmv0BaunQpw4YNc9lOKcXevXutC6T777+fxMREl0l63759+eSTTwA4c+YMkZGRlyX8eV/PPvssPXo4fiQTExOZNWuWy2PWrFnTOk8An332GRkZGZe18/b2pn379tZiEcePH2fdunVFXiC1adMGPz8/q21mZmaRbX08eN4z4JjTXSjpnr68KS//rz0HTwYQFpzBm3dtZmwf5wqGOTmO9q1be/Scb1ExNG7cmMcff5zHH3+cI0eOMGPGDGJjY1m2bBmJiYlWf5K/akbeoIDwHDVq1OD222/n9ttvJyMjg127dllJ9/Hjx7n//vvJycmhQ4cOxMTEYBgGrVq1sjlq4cncPuKtlPoZ+Fxr/Xmh7RNwjHD3dL4OBE4AnbXW2690TE8YPXEXrbWVzHt5eVlJ2sWLFzlx4kSRFxPt27e32v76668cPXrUZeIfGhrKwIEDAUfi/dlnnxV5zLFjx9KmTRvAkaTmXcwU/goMDOTLL7+0PkNMTAxHjhxx2fahhx7iqaeeAmDRokXcddddLt9ba01KSor10ExUVBQJCQkuz1lERARz584FHHVgGzRoUOT5nTFjBqNGjQJgypQp/OUvf3HZLiQkhGPHjlmvmzZtyqFDh1y2nTRpknVnIjExscDof2EHDx6kadOmAIwePZqZM2e6bDds2DDmzZsHOH55hIeHF3kx8fHHHzNkyBDAcYHwySefuEzig4OD+frrr633eOqpp1zecfD29mbw4MHWXaM9e/YQGxvrcoqVj48PMTEx1kNsq1at4tixY47jHD+Oz759+AA+3t4kbb+B12INMi75ANnAXqr5wttjtxLT4yjeXl74+Pnh06kTgW3besyFh4x4e5Zjx44xc+ZMTNNk8eLF1qCFl5cX/fr1wzAMRo8efcV+RHiG/fv388orr5CQkMBvv/1mbW/bti2GYfDYY48VGIgTVYNHjXgrpbyBrkCCUmo3UA2YCTwLtAWsGk9a6/NKqT3O7VdMvMXv8s9zz69atWrFfnK7Y8eOxWoXEBDAE088Uay2ERERREREFKttbGxssdoNGjSI48ePu9yXm5tbYK77119/zcWLF10m89WrV7fa1alTh+XLlxd5Z6Jbt24FPlP9+vVdHrNwXeDx48dz+vRpl23zL40cHBzMkCFDLnvfvK/8i37UrVuXxo0buzxm3kUUQHZ2NhkZGUWex8zMTOv/Dx06RFEJUeFE4vvvvyctLc1l28DAQCvx3r59e5FTpwCGDBliJd5vvvkms2fPdtmuuu9QLmTd6Xx1AriRi1nw1BeOr/xmzpxJVFQU4Jg6NXnyZJdJf4MGDdi4caP1fQMHDuTkyZMuLxLuu+8+7r//fsAxdeqdd94p8mJi8uTJ1jzgb775hl27dhX5HIjwLPXr12fChAlMmDCBkydPMmvWLEzTZNGiRSxevJjFixfz6KOP0rdvXwzDYMyYMQXuqArPER4ezrRp08jMzGTRokWYpsnMmTNJTk5m27ZtPProo1bbI0eOEBoaKs9Yiaty95BQKOALGEAfIAuYBUwCagCFs6izgMuim84R8gmAPIUsLlN4jnfNmjWLVb/Vz8+P3r17F+s9unTpQpcuXYrV9vXXXy9Wu5tvvpkFCxYUq+3nn39+9UY4EuZz5865TNBzcnIKPEQ0YcIERowY4bJt4Yu5f/3rX0Ue95ZbbrHaNW/enOeff77Ii4nAwECrbffu3QHHxUL2sWNkX7xIdm4u2Tk5/LKzbaFP1gLHyHc2DeucIzsnh+ycHHKgwAVK/ilY+S8ywPE8RH7JyckcPXrU5Xns27ev9f+HDh3i+++/L+qU89JLL1n//80331h3VAqLjIws8hii4gsODmb8+PHWhfXs2bMxTZMFCxawbNkyli1bxuOPP06vXr2sqhl5d6yE5/D392fEiBGMGDGCTz75hCVLlrB582ZralFubi7dunXD398fwzAwDIMuXbpIEi5ccutUE6VUHeAUjiklXzq3ReNIvH8CfLXWj+Rrvxl4TWsdd6XjevJtSyFEEZKSHDW7ncIfieDAicDLmjWrd579Hyb+vqFRI0d1E6fc3Fwr8XY1JSn/8xXbtm0r8BxI/u8JDw/n+uuvBxwP3q1YsaLIi4kHH3zQWojlm2++YceOHS4vUNq1a8cjjzwiU00qmd9++81KwufNm1fggu/mm2+2knC541E57N+/n+7duxe4AxseHm4l4d27d5ckvBLxuKomSqkU4GWt9VfO13mJ90fA/VrrXs7tgThGwG+SOd5CVEH79sGGDdbDldOXN2XCJ12dc7wdAvyy+fShtb8/YOntDZ07gwclNDLHu3JLT08vUDXjwoUL1r4uXbpYyVn+h/GF58nJyWH58uWYpklcXBxHjhyx9q1YsYJe+QYDhGfzxMR7MjAcGIFjqkkCsBR4D9gNjAfmAq8Dt2qtr1rVpCp14kJUGdnZkJBQ/Kom4Ei8R470qKomknhXHefPn2f+/PmYpsns2bM5f/68ta9Tp05WEn7jjTfaGKUordzcXH7++WdM0yQpKYmVK1da09rGjRtHzZo1MQyD3r17XzbdTVR8nph4+wL/Au4GLgLfA89prS8qpQYB7wPN+L2O9/6rHbOqduJCVHpSx7tSkj4bLly4wMKFC4mNjSUhIYH09HRrX7t27awkvE2bNjJNoZI4deoUoaGhZGdnAxAaGsqYMWMwDIO+fft6TCWmqs7jEu/yIJ24EJWUrFxZKUmfXVBmZiY//PADpmkya9Yszpw5Y+1r1aqVlYR36NBBknAPprVm7dq1mKaJaZrs3bvX2levXj1iY2Pp16+ffQGKYpHEGwgICNBt2rS54kIiRS3VXpHbSQcrBI7ke8sWx8g3FEzAfXwc+1u0gHbtPC7pBkm8RUGXLl1i8eLFmKbJjBkzCqxU3KJFCysJ79y5s/yO8GBaazZu3IhpmsTGxrJ7925SU1Otsq3ff/89NWrUYNCgQQXKwwr7SeJN0UvGezovL69SJfIV6SLiWtt5eXnJLxfhkJ0NKSmOSidZWeDr66hg0rSpR83pLkwSb1GUrKwsli1bhmmaxMfHF6iacd1111lJeLdu3aSf9GBaa/bu3WtVTdJaEx4ezsGDBwkKCmLkyJHExMQwePDgy9aIuKL8fealS+DnVyn6TLtJ4g20adNGf/XVVy7LdRW1EIontBMOdl4YVJSLksJ1yUXlIYm3KI7s7GyrakZ8fHyBqhlhYWFER0djGAY9evSQ/sLDXbx4kXfffRfTNNm0aZO1vWbNmtx2221MnDiRzp07F32AK90lzHuY04PvEtpNEm8qZyeutSY3N7fUiXxFuYi41na5ubl2/1VUCPlXJK0oFwbufm9vb+9KOaonibcoqZycHKtqRlxcHIcPH7b2NW7c2ErCe/bsKVUzPNzOnTutOeEbNmwA4Mcff2TAgAEAHDhwgHr16v2+EFkVeC7GbpJ4I514ZZa3+ImdFwZ2X5RkZWXZ/ddQYeQl5hXhLkRZXbzcdNNNkniLa5abm8uqVauIjY3FNE1SUn4vr9mgQQOrakafPn3w8ZHpBZ5sz549zJo1iyeeeML6uxwxYgRLliwhIiICwzAYER5OzdTUSl0Jym6SeCOduKj8rnT3o6pclOQU5xeJZ5LEW5QJrTVr1qyxRkj37dtn7QsJCWH06NEYhkG/fv3w9fW1MVJRFnJzcxkwYADLli2ztvn7+jKsY0eMHj24rWtX5qy7sdKtfWA3tyXeSqmFwGAgWmsdn2+7AqYC9wPvaK1fuNZgrpVHd+LZ8vCDEMWhtS6QlFfEqVHX0m7jxo2SeIsyp7Vmw4YNBapm5AkODmbUqFEYhsGAAQOkaoaHO3ToEHFxcZhff03SunXk5XX39n2auJX/r9Kt9ms3dybeHYH1wA6gvdY6x7n978Cfgf9orSdcayCl4ZGduDz8IIRA5niL8qe1ZvPmzVYSvn37dmtf7dq1iYqKwjAMBg8ejL+/v42RilJJSiJ1yxZmrF6NuWoVu1JncPh0C+fOV4A1QAxN6g4i5ePVv39fo0aOud6iWNw61UQp9QWOke0/aK2/UEq9BLyJY/XJu7TWtjwJ53GduDz8IIRwksRbuFtycrI1HWXLli3W9lq1anHbbbdhGAZDhw6levXqNkYpSmzJEjhxwnrpdYeB1nm5w43ATuf/ezOofRuMHj0Y1a0boS1agCzcU2zuTrybALuAo8DfgH8DC4CRWutL1xpEaXlcJ14FlsEWQhSPJN7CTtu3b3dMUzBNNm7caG0PDAwkMjKSmJgYhg8fTkBAgH1BiuJJSnJMW3UKfySCAyec1U44AcwCYoEfAUfJYi+leOdPf2LiBx+4OVjP5faHK5VSbwF587h/BgZrrTMKtekLTAS6AI1wjpBfa5BX41GdeHY2JCRYSXfSH4PJOnt5zVXfoFx6/eek44U8/CBEpSWJt6godu/ebSXh+f9+AgICfq+aMWIENWrUsDFKUaR9+2DDBiu/mL68KRM+6XrZHO9/3P8j/r4JmCtXsnDTJuZ+/jmD77sPgIULF7Jt2zaio6Np0qSJLR+jorMj8f4z8Hfny9Za6+0u2kQAvXHMCf8KeKQ8E++QkBAdExNzWZmuwMBAXn75Zavdf//7X9LT012W/Wrbtq1VkP7EiROsXbu2yPJfrVu3tubBHTt2jMzMzCLbXvbkeKF/GEtvDynyc/X73rlKmTz8IESlJYm3qIj27dtnJeGrVq2ytlerVo1hw4ZhGAaRkZEEBQXZGKUooNDAHjiS7ytVNTlz8SI1YmLwca6IGRUVRUJCAgC33HILhmEQHR1Ns2bN3PtZKjB3TzW5C5iOY6pJA+BjrfWfrvI954DHyjPxLmrJ+Nq1a3P69GnrdfPmzQuUV8rv+eef5+233wbghx9+YMiQIUW+3549e2jevDkAMTExmKbpst3AgQNZtGgRAGfOnKFRo0b4KIW3Uvh4eeHj7U3OGV+88eZJnqQHPQCYz3xmMpM6LXLx8fZ2fFWvjk+9egQFBfHtt99a7/HnP/+ZU6dOubyYGDhwIBEREYCjE/3222+LrDVsGAa1atUCYPXq1Rw5csTlhUSdOnVo3bo14FjEYffu3UXWLg4MDJSSVUJchSTeoqI7ePAg8fHxmKZJUlKStd3Pz48hQ4ZgGAYjR46kTp06NkYpgFJPZY2Li+N///sfiYmJXLhwwdrerVs3nn76ae66667yiNqjlLbPLvbcBeco9pdAMjAA+Al4UCn1L1ej3u4UFhbG888/f1mZrsJJ3x/+8AdOnDjhsqRXp06drHZ16tRhyJAhRZYEq+a8MgSoW7cuTZo0cXnM/E+HZ2dnF/ghLuwSv0+RP8EJdrDDMZu+kODg4AKv4+LiOHjwoMtjVqtWzUq8d+7cyUsvvVTk+w8YMMBKvN955x3i4+Ndths8eDALFy4EHBcTrVq1KvKYpmkSHR0NwFtvvcUrr7ziMkmvV68eycnJ1vcNGTKEo0ePurxIGDt2LA888AAAv/76K2+88UaRdxtee+0163x9++237Nixw2W7Zs2aERkZCUBmZiYzZswo8mKiXbt2hIQ47lIcPXr0sjjz3+nIaweQlZWFt7e3LOUshPA4YWFhPPXUUzz11FMcPnyYGTNmYJomP/30E3PmzGHOnDn4+voyaNAgDMMgKirqst9Vwk3atYOzZ4tfvKFduwKbo6OjiY6O5ty5c8ybNw/TNJkzZw5r1qzhyJEjVrvDhw9z/vx5WrZsWV6fpNIqVuKtlOoNmMAhYIjW+rhS6hUcs/TfBkaVW4TFEBISwiOPPHLVdq+88kqxjte1a1cWLFhQrLaffPJJsdoFBwdz/vx5slesIPvwYbJzcsjOyWHFw3XIIYfa1LbaDmc4XelKxzdOWu2y69Qh+8YbL0vc/vGPf5Cenu4y8b/55putduHh4bzwwgtFXkzUrFmzwOfPyspyecw2bdoUeP+WLVsWWb+48IVHTk4OOTk5ZGZmFjhG4bsu27Zt49ChQy7PY8+ePa3/T01NLfJuA8Bzzz1XIPGeNWuWy3bDhg2zEu+zZ89e8Yo+Li6OMWPGAPDZZ58xadIkl+3q1avH8ePHrdfXX389KSkply397u3tzcSJE60pUcuWLeOBBx4o8mLCNE0aNGgAwF//+ldWrVrlsl2HDh148sknATh37hxTpkwp8mJi5MiR3HDDDYDjYmbjxo0uj1m9enVrmWKADRs2kJOT43LVxjp16lC7dm3AcdGRmZlZ4H0r49LvovjOnj3LxYsXCwxiCM/QuHFjHnvsMR577DGOHDnCzJkziY2NZenSpcybN4958+YxYcIEBgwYgGEYjBo1ivr169sddtWhlKMSWlHlin18HJXVrlKuuEaNGsTExBATE0NGRgYLFiwokFN8+OGH/PWvf6VDhw4YhoFhGNbdcHFlV51q4qzfvQy4APTWWu/Jt28N0BXoq7VeXsT3l/tUE4+6bVlF53gXXvwkf5Kem5tLw4YNrbY7duzg4sWLLhP/Zs2aWUliWloay5cvL3LBkj/84Q/WQ0Dffvst27Ztc3mB0Lp1ax599FHAMYr/8MMPF3kxMWXKFHr37g04Lro++OADl3HWrVu3QJmuZs2aFXln4i9/+Quvv/46AHPmzOG2224r8jweOnSIxo0bAwXn4hUWERHB3LlzAThy5EiB81vYzJkziYqKAmDy5Mm8+uqrLtuFhoYWGPFo0qQJhw8fdtl20qRJTJkyBYDExERGjBhRYL+Xl5eVhO/YscN6iGf8+PEsWrTIZeLft29f/u///g+A06dPc8cdd7hcmt3b25unn36aLl26ADB//nzmz5/v8pi1atXiiSeesOKaNm0aFy9edHnM1q1bW79YTp48SXJysst2Pj4+NG/e3LrjdubMGZcXKF5eXiilquRUE6WUrlmzplW6btiwYVK6zsMdP36cmTNnYpomP/74o7XSrJeXF7feeiuGYTBmzBhr4EC4Qf4F+rKywNe3zBbomzRpEu+//z5nz561trVp0wbDMLj99ttp27ZtaaOvsMp1jrdS6gZgBeAP3Kq13lRo/yDgB2CV1rpHEceQxDs/qWpSpbla+t3f35/AQEfJp/Pnz5OamlrkxUS3bt2sOwmrV68mLS3NZeLfuHFjhg0bBkB6ejoffvhhkasrjh8/nnbO240zZ85kxowZLtsGBQXxzTffWJ9lxIgRHD161GXbJ554gj//+c+AI/G+/fbbC7xnfqmpqdaFQWRkpHXBUNhtt91mXWikpqZaFyCuJCQkWBcwr776KpMnT3bZrkGDBqSlpVmvGzVqVOB1fq+++iqvvfYacPULpMOHD9OoUSMr7jlz5hT5mWbPnl3lEu+AgACdf+pdYGAgI0aMwDAMRo8ejY/0dR7t5MmTJCQkYJomP/zwA1lZWQAopejTp4+VhF/p37Co+C5dusSiRYswTZOZM2daz9SNHz+ezz//HHA8C5Y3yFBZuL2qSYnfQBLvy0kdb1GFaa0LXIBUr17dmkJ14sQJMjIyXCbzNWvWpEULxypsFy9eLHC3o/DFRL9+/axR9F9++YWVK1e6vOMSEBBQ4NmH5557jtOnT7s87p133skdd9wBwIoVK3jxxReLvIuTlJREvXr1ALj77rtZsGDBZe1yc3MZOXIkCQkJVS7x7tq1q/7uu++sqhlr1qwBoGHDhhw6dMj6eZDpKJ7vzJkzzJ49G9M0mT9/Ppcu/f48U8+ePa2qGWFhYTZGKUorKyuLJUuWYJomd955pzUt8X//+x+vvPKKNR2lS5cuHp+EV8jEWylVA7jB+fJnHPPAE4BTWmvX99tLweMSb1m5UogqLzc3l9zcXHx9fatk4p2/zz5w4ABxcXF4e3tbzyacOHGC8PBwBg4ciGEY3HbbbdZzA8Iz/fbbb8yZMwfTNJk3bx4XL1609nXv3t1Kzq7z4GmVoqBx48bx5ZdfWq+bNWtm/T13797dIwsOVNTEux+wxMWuL7XW48r6/Twu8QZH8l3Khx+EEJ7P0+d4K6XqAp8DQ3Asj/ei1vqbK31PcfrshIQERo0aZT187evrW6B0Xd26dcvmAwhbnDt3jsTEREzTZO7cuWRk/L4OX5cuXayR8Ly7XMIz5d0BNE2TuLg4UvOtrJl/+qAnqZCJt7t5ZOKdpxwffhBCVHyVIPH+H+AFPAB0AuYCPbXWyUV9T3H77LS0NKt+9E8//URubi4A/v7+HD16VBZvqSQyMjKYP38+sbGxzJkzh3Pnzln7OnbsaI2QXql8raj4cnNz+eWXXzBNE9M0eeKJJ3j22WcB2Lp1Kx999BGGYdC7d2+8vb1tjrZoknjj4Ym3EKJK8+TEWykVCJwG2mmtdzq3TQMOa61fKOr7rqXPPnr0qFU1AxwLnYHjl/nYsWO59dZbGT16NKGhocU/aP6Bj0uXwM9PBj5sduHCBRYuXIhpmiQkJPDbb79Z+9q2bWsl4W3btvX4ucJVWW5uLllZWVaxgPwPwdevX58xY8ZgGAa33nprhXvYWhJvJPEWQnguD0+8OwM/a62r59s2EUcVrCLLvpS2z87JybFGxFatWkWPHo6iWl5eXvTt29eqmlFkGc0rTfXLG2mTqX62y8zMLFA148yZM9a+Vq1aWUl4hw4dJAn3cJs3b+abb74hNjaWPXusqtUEBwczbtw4/va3v9kYXUGl7bM9b1a7EEKIiqIGcLbQtrNAzcINlVITlFJrlVJr8y8udS3y34Zu1aoVU6dOZcSIEXh7e7N06VIee+wxGjduTJ8+fS4vD5n3cHteZanCD7jnbdu1y9GuEgxOeSp/f39GjBjB1KlTOXr0KPPnz+fBBx8kODiY7du388Ybb9CpUydatmzJiy++yLp16y5bkE14hvbt2/PWW2+xa9cuNmzYwMsvv0zLli05efJkgcXozp8/T2JiYoHqOJ5GRryFEMJGlWDEO0lrHZBv2zNAv/Ic8S7K2bNnC5SuCwoKIjU11UrUTdOka40ahGdkSDlXD5adnc3SpUsxTZP4+PgCiVl4eHiBqhkyEu65tNYkJydbC5gBxMbGcvvttxMUFMTIkSMxDIMhQ4a4teyoTDVBEm8hhOfy8MQ7b453W631Lue2r4DUsp7jXVLp6ens2LGDrl0dp/b06dPUr1+f7Oxsul5/PTE9ehA+axj1zzW57HtlATPPkZOTw/Lly62qGflX123atCnR0dHExMTQo0cPjyxdJwqKj4/n9ddfZ9Om39dzrFGjhrUK7ujRo8v9YksSbyTxFkJ4Lk9OvAGUUt8CGngQR1WTRMqoqklZ2r9/P88/9hhzf/yR8/nqR7egBX3py23cRhC/V0np971zFNXbGzp3BqktXeHl5OQUqJpx+PBha1+jRo2Ijo7GMAx69epVoatmiKvbuXOntQDX+vXrAbjppptYt26d1SYjI4OAgICiDnHNJPFGEm8hhOeqBIl3XeC/wGDgJPBCWdTxLhdJSWTs28eCX3/FXLmSmSvWk0EGCsX3fE89HKuNnuUsUd/nm0PaqJFjITPhMXJzc1m9ejWxsbGYpsnBg7+v3RcaGmpVzejbt2+Fq5ohSmbv3r3ExcXRoEED7r33XsBRnrBr164MHz4cwzCIjIykZs3LHj25JpJ4I4m3EMJzeXrifS1s67OXLIETJ6yXC28PYi1r2cMe7sXxC1ujuZu7qdPYG6NHD2JuuYV2N92E6t/f/fGKMqG1Zu3atdZI+N69e6199erVY/To0RiGQf/+/fH19bUxUlFWPvvsM/74xz9ar/39/Rk6dGiZrILrsYm3UqoFsBkwtdb3OLcNBD4AwoBVwDit9YGrHUsSbyGEp5LE242Skhw1u52W3h5yWZOjHOWP/JF00q1tLZs2xbj3XgzDoFOnTvLAngfTWrNx40ZM0yQ2NpZdeSUlgbp16zJq1CgMw2DgwIH4+fnZGKkorUOHDlkLcK1YscKqeBMcHMyRI0eu+U6HJyfeC4HqwAGt9T1KqXrAHhzzBGcDU4A+WuseVzuWJN5CCE8libcb7dsHGzZYFU1cJd4A2WST+/JSzJUrmbFmDSfyLeKydOlSbr31VreEK8qX1potW7ZYSfi2bdusfUFBQURFRWEYBoMHD3Zr1QxR9tLS0pgxYwamadKkSRO++uorwDEP/M4772TkyJGMGjWKevXqXfVYHpl4K6XuBMYAW4EbnIn3BBwj3D2dbQKBE0BnrfX2Kx1PEm8hhKeSxNuNsrMhIcFKvJP+GEzW2csrXeSvapIN/FS7NuaMGfz0009s3LjRGil7+OGHCQwMxDAMbr75Zqma4eG2bt1qTUfZvHmztb1mzZpW1Yxhw4ZRvXr1KxxFVHS5ubnWv9X4+Hiio6MBx/oA/fr1s6qjFLUKrscl3kqpWsBaYCDwAL8n3v8C/LTWf8rXdgvwqtY6zsVxJgATAMLCwrocOHDVGSlCCFHhSOLtZps3/754ztUUquOttbammZw9e5b69etbC3k0btzYqprRs2dPqZrh4Xbs2GFVzdiwYYO1PTAwkMjISAzDYPjw4QQGBtoYpSitU6dOWSPhixYtIjs7G3CsgtunTx/mzp172d+xJ65cOQX4XGudUmh7sVdAA9Baf6q17qq17hoS4vp2oRBCCFFAu3ZQv/7vS8MXxdvb0a5dO2tT/rndNWvWZPHixTz11FM0adKEw4cP895779G3b1+aNGnCkiVLyusTCDe48cYbeemll1i/fj27d+/m7bffpmvXrpw/f57vvvuOmJgYQkJCMAyDb7/9lvT09KsfVFQ4devW5YEHHmDevHkcO3aML774gsjISHx8fDh9+nSBpPvLL78kJaVw6lpybh3xVkp1AqbjmD5ySSn1GgVHvH211o/ka78ZeM3ViHd+MtVECOGpZMTbBlrDli2OkW8oOPrt4+PY36KFI+kuxoOUubm5rFmzxpqmsH//fg4ePEjTpk0BmDlzJjVq1KBfv35Sus7D7d+/3xoJX7lypbXd39+fYcOGWVUzgoKCrnAUUdGdPXuWlJQU2jkvvHfu3MmNN96Yt9tzppoopZ4C3gTrcfEagDewDfgYuF9r3cvZNhA4Dtwkc7yFEJWVJN42ys6GlBRHpZOsLPD1ddTsbtr0mleq1Fqzbds22rRpY71u2bIlu3fvJjg42CpdN2DAACld5+FSUlKsqhlJSUlW1Qw/Pz+GDBmCYRiMHDmSOnXq2BypKK3k5GReffVVFi9ezOnTpz0q8Q4AauXbNBEIB/Lmde8GxgNzgdeBW6WqiRCiMpPEu3LLzMxkypQpxMbGsnPnTmt7nTp1iIqK4sknn6RTp072BSjKRGpqqjVX+KeffiI3NxcAHx8fBg0ahGEYREVFFatqhqi4srKy8PPz85zE+7I3zzfVxPl6EPA+0Izf63jvv9pxqlInLoSoXCTxrhq01iQnJ1vTUZKTkwGYP38+Q4cOBeDw4cMEBwdL6ToPd/ToUSsJX7JkiZWEe3t7079/f6tqRv369W2OVFwLj6tqUh6qYicuhKgcJPGumrZt28bMmTOZOHGiNeVk9OjRLFq0iMjISGJiYhg2bBgBAQE2RypK4/jx48yaNQvTNPnxxx8LVM3o27cvMTExjB49moYNG9ocqSguSbyRTlwI4bkk8RbgeECzb9++JCUlWdsCAgIYMWIEhmEQERFBjRo1bIxQlNapU6dISEjANE0WLlxIVlYW4KiW07t3bwzDYMyYMTRp0sTmSMWVSOKNdOJCCM8libfIb+/evVbVjNWrV1vb//a3v/HMM8/YGJkoS2fOnGH27NmYpsmCBQvIzMy09t1yyy0YhkF0dDTNmjWzMUrhiiTeSCcuhPBckniLohw4cID4+HhiY2P55ptvCA8PB+CNN95g9erVVtWM2rVr2xqnKJ3ffvuNuXPnYpomiYmJXLx40drXrVs3DMPAMAyaN29uY5QijyTeSCcuhPBckniLkmrbti1bt24FwNfXl8GDB1tVM+rWrWtzdKI0zp07x7x58zBNkzlz5pCRkWHtu+mmm6wkvEWLFjZGWbVJ4o104kIIzyWJtyipI0eOMGPGDGJjY1m2bFmB0nVvvfUWEydOtDlCURYyMjJYsGABsbGxzJ49m3Pnzln7OnToYCXhrVu3tjHKqkcSb6QTF0J4Lkm8RWkcO3aMmTNnYpomixcvZtasWYwYMQKApUuXsn37dkaPHk1oaKjNkYrSuHjxIgsXLsQ0TWbNmsVvv/1m7WvTpo2VhLdr1w5VjNVWxbWTxBvpxIUQnksSb1FWTp48Sc2aNfHz8wPAMAzi4uJQStG3b1+rakajRo1sjlSURmZmJj/++COmaTJz5kxOnz5t7bvxxhutJLxjx46ShJcDSbyRTlwI4bkk8Rbl5bvvvuPrr79m4cKFXLp0ydreq1cvHn/8ce644w4boxNlISsriyVLlmCaJjNmzODEiRPWvuuvv95Kwrt06SJJeBkpbZ/tVZbBCCGEEKJiuOOOO5g9ezbHjh3j66+/ZtSoUfj7+5OUlMT+/futdkePHi3wWngOX19fhgwZwqeffkpaWhqLFi3i4Ycfpn79+uzZs4d33nmHbt26cd111zFx4kRWrlxpPRMg7CEj3kIIYSMZ8RbulJ6eTmJiIj179qRp06YAvP7667z22mt07drVqh99ww032BypKI2cnBxWrFiBaZrExcWRlpZm7WvSpAnR0dHExMRwyy234OUlY7AlIVNNkE5cCOG5JPEWdnvppZd47733OH/+vLWtU6dOGIZBTEwMLVu2tDE6UVq5ubn88ssvxMbGEhcXx6FDh6x9DRs2JDo6GsMw6N27N97e3jZG6hkk8UY6cSGE55LEW1QEFy5cYMGCBZimSUJCAunp6QDcc889TJs2DQCttcwT9nC5ubmsXr0a0zQxTZMDBw5Y++rXr8+YMWMwDINbb70VHx8fGyOtuCTxRjpxIYTnksRbVDSZmZn88MMPmKbJXXfdxdChQwGIi4vjlVdesR7Ya9++vSTiHkxrzbp16zBNk9jYWPbu3Wvtq1evHqNHj8YwDPr374+vr6+NkVYskngjnbgQwnNJ4i08xfjx45k6dar1ukWLFlYS3rlzZ0nCPZjWml9//dVKwnfu3Gntq1OnDqNGjcIwDAYNGmSVq6yqJPFGOnEhhOeSxFt4iqysLJYtW0ZsbCwzZszg+PHj1r6RI0cya9YsG6MTZUVrzZYtW6zpKFu3brX2BQUFMXLkSAzDYMiQIVSrVs3GSO0h5QSFEEIIUe58fX0ZNGgQn3zyCampqSxevJhHHnmE0NBQbr75Zqvdrl27eOaZZ/jll1+kdJ0HUkrRvn17Xn/9dZKTk0lOTmby5Ml06NCBs2fPMm3aNKKioggJCeHuu+9mxowZXLhwwe6wPYaMeAshhI1kxFt4upycHDIzMwkICADgzTffZNKkSQA0btzYqprRs2dPqZrh4Xbu3ElcXBymabJ+/Xpre2BgICNGjMAwDCIiIggMDLQxyvIlU02QTlwI4bkk8RaVzcaNG5k2bRqmaXLw4EFre4MGDbjvvvt45513bIxOlJW9e/da01HWrFljba9evTrDhw/HMAwiIyOpWbOmjVGWPZlqIoQQQogKo1OnTvz9739n//79rFq1imeffZbw8HCOHDlCSkqK1e7ChQssWrSI7OxsG6MV16p58+Y899xzrF69mv379/P3v/+dW265hQsXLhAfH8/dd99NSEgIUVFRTJs2jbNnz9odcoUgI95CCGEjGfEWVYHWmvXr1+Pn50f79u0BmDVrFqNGjSI4ONiqmjFgwIAqXzXD0x06dIj4+HhM02TFihXk5Zl5y9sbhsHIkSOpW7euzZFeG5lqgnTiQgjPJYm3qKri4uKYNGkS27dvt7bVrl2bqKgoDMNgxIgRUqLQw6WlpTFjxgxM02TZsmXWw7Y+Pj4MHDgQwzAYNWoU9erVsznS4pPEG+nEhRCeSxJvUdUlJydbc4W3bNkCQPv27dm0aZPVJjMzE39/f7tCFGXg6NGjzJw5E9M0WbJkCTk5OQB4e3vTr18/DMNg9OjRhIaG2hzplUnijXTiQgjPJYm3EL/bvn07cXFxNGjQgAceeACA3bt307lzZyIjIzEMg+HDh1sVVIRnOnHiBLNmzcI0zQLz/L28vOjbt6+VhDdq1MjmSC8niTfSiQshPJck3kJc2X//+18rCQcICAggIiLCmo5So0YNG6MTpXX69GkSEhIwTZOFCxdy6dIlwFFPvFevXhiGwZgxY2jatKnNkTpI4o104kIIz+WpibdS6jFgHNAe+J/Welxxv1f6bFFS+/bts+pHr1q1ytoeHBxMWloavr6+NkYnysrZs2eZPXs2pmkyf/58MjMzrX09evTAMAwMw6BZs2a2xSiJN9KJCyE8lwcn3mOAXGAoUF0Sb+EuBw8etKpmNG7cmO+++w5wzAO/9957iYyMZOTIkdSuXdveQEWppKenM3fuXEzTJDExscDqmN26dcMwDKKjo7n++uvdGpck3kgnLoTwXJ6aeOdRSr0BNJHEW9ghOzsbHx8fAObOnUtkZCTw+/L2hmEQFRVFcHCwnWGKUjp//jzz5s3DNE3mzJnD+fPnrX2dO3e2RsJbtmxZ7rFI4o104kIIz1VVEm+l1ARgAkBYWFiXAwcOuCE6UZUcP36cuLg4YmNjWbp0qVW6ztvbmwEDBhAXF1fpVlGsijIyMliwYAGmaTJ79mzS09Otfe3bt7eS8DZt2pTL+3vUypVKKX+l1OdKqQNKqXSl1Aal1PB8+wcqpbYrpTKUUkuUUvZN4hFCCFFmtNafaq27aq27hoSE2B2OqIRCQkJ4+OGH+fHHHzly5AiffvopQ4YMASAlJaVA0v3dd99x5MgRu0IVpRAQEMDo0aOZPn06x44dIyEhgfvuu4+goCA2b97Mq6++Stu2bWnTpg2vvvoqmzdvpiINMrt1xFspFQg8C3wBHAQigP/heDjnHLAHeBCYDUwB+mite1ztuDLiLUTll9QgiayjWZdt9w31pdeRXjZEVDYq4oi3UmopcGsRu5O01r3ztZWpJqJCO3nyJAcOHOCmm24CYP/+/Vx33XUopejTp49VNaNx48Y2RypK49KlS/z444+YpsnMmTM5deqUta9ly5bWSHinTp1KtTCTx081UUptAl4HgoFxWuuezu2BwAmgs9Z6+xUOIZ24EFXAUrW0yH39dD+3xVHWKmLiXRKSeAtPk5yczEsvvcT8+fOt0nUAvXr1Ijo6mgcffFCmpHi4rKwslixZgmmazJgxgxMnTlj7mjdvbiXhXbt2LXES7lFTTQpTSoUCLYFkoC3wa94+rfV5HCPgbe2JTgghRFGUUj5KqWqAN+CtlKqmlPKxOy4hrqZt27bMmjWL48ePM336dEaPHk21atVISkripZdeKpCInT592sZIxbXy9fVlyJAhfPrpp6SlpfHjjz/ypz/9idDQUPbu3cv/+3//j+7du3PdddfxzDPPsHLlSuuZgPJm24i3UsoXmAfs0Vo/pJT6HDiutX4hX5sk4D9a6y9cfL88qCNEFSIj3hWLUuo14NVCm1/XWr92te+VEW9R0Zw7d47ExEQOHjzIxIkTAcfUhQYNGhQYIb3hhhtsjlSURk5ODklJSZimSVxcHKmpqda+Jk2aEB0djWEY9OzZEy8v12PTHjnVRCnlBXwD1AKitNZZSql/Ab5a60fytdsMvKa1jrvS8aQTF6Lyk8S78pA+W3iCTZs20atXL86dO2dt69ixIzExMRiGwY033mhjdKK0cnNzWblyJbGxsZimyaFDh6x9DRs2ZMyYMRiGQZ8+ffD29rb2eVzirRz3cP4LhAMRWusLzu0TgPu11r2crwOB48BNMsdbCCGJd+UhfbbwFBcuXGDhwoWYpklCQgK//fabtW/r1q20bt3axuhEWcnNzWXNmjWYpolpmuzfv9/aV79+fUaPHo1hGPTr1w9fX1+PS7w/BjoBg7TW5/JtDwF2A+OBuTgeuLxVqpoIIUCqmlQm0mcLT5SZmcmiRYswTZOtW7eycuVKaz74vffeS3h4OIZh0KFDh1JVzRD20lqzfv16TNMkNjaWPXv2WPuCg4M5efKk5yTezrrc+4FMIDvfroe01tOVUoOA94FmwCocVU72X+240okLITyVJN5CeB6ttZVcp6SkEBYWZu1r0aKFNSe8c+fOkoR7MK01mzZtspLwHTt2AHhO4l1epBMXQngqSbyF8GzZ2dksXboU0zSJj4/n+PHj1r7rrruO+Ph4OnXqZF+AokxorUlOTqZ9+/aeW05QCCGEEMKT+fj4MGjQID7++GPS0tJYsmQJjz76KA0aNODw4cM0b97cavvdd9+RlJTkttJ1ouwopWjXrl2pjyOJtxBCCCFEGfD29qZfv368//77HD58mPXr11OrVi3AsajLo48+Su/evWnatClPPPEEP/30Ezk5OTZHLdxJEm8hhBBCiDLm5eVF27a/rwGYkZHB/fffT1hYGKmpqfz73//m1ltvpUmTJjz66KPs2rXLxmiFu0jiLYQQQghRzoKCgvj73//O/v37Wb16Nc899xzNmzfnyJEjfPjhh6Snp1ttU1JSyMq6vIqT8HyyvK8QQgghhJsopejWrRvdunXj7bffZuPGjcyfP5/OnTtbbe666y62bdvGqFGjMAyDgQMH4ufnV7I3ys6GlBRITYVLl8DPDxo1gqZNwUfSP7tIVRMhhLCRVDURQuR38eJFunTpwtatW61tQUFBREVFYRgGQ4YMwd/fv+gDaA1btkDe1JX8c8jzVmBs0QLatQMpdVhipe2zZaqJEEIIIUQFUa1aNZKTk0lOTub111+nffv2nD17lq+++oqRI0fy7bffFv3NWkNSkiPpzskpmHTD79t27XK0qwSDr55GEm8hhBBCiAqmTZs2/OUvf2HTpk1s376dN998k27dujFy5EirzcSJE7njjjswTZPz5887RrqPHbs84S4sJ8fRbsuWcv4UojCZaiKEEDaSqSZCiGuRm5tLw4YNOXbsGADVq1cnomNHjJtvZsRNN7HpiSZknb18fNU3KJde/znpeOHtDSNHypzvEpCpJkIIIYQQVYyXlxerVq3ib3/7Gz169ODChQvErVzJXf/6F/UffJCEswtcft9lyXhKihuiFXkk8RZCCCGE8EDh4eE888wz/PLLLxyMi+P/xo2jd6tWZGZn04xmVrtf+IX5zCed9IIHyMlxVD0RbiP3FoQQQgghPFzTOnV4MiKCJyMiSD11ih0Pt7T2fcd3/MqveONNF7owYfFNRHXtSr1atUDqhbuVJN5CCCGEEJ4uX53vRnXrspPfSwUOZCDeeLORjaxmNas/Xs1DXl70b9uWP993H8P79bMh4KpJEm8hhBBCCE/XqBEcPeqyosltzv/OcIYkktjc8Qd+3LKFRZs3c2d2ttUuJSUFHx8fGjZs6M7IqxSZ4y2EEEII4emaNi3w0jco97ImtanNqKDhzH/5ZY7+5z9MffRRRv3hD9b+d955h8aNG9O3b1/ee+89Dh06VO5hVzUy4i2EEEII4el8fBwrUjoXz7FKBhahblAQ4x56CEJDrW2XLl3Cz8+P5cuXs3z5cp588kluueUWDMMgOjqaZs2aXeGIojhkxFsIIYQQojJo1w7q1/99afiieHs72rVrV2Dzp59+yrFjx/jmm28YM2YM1apV45dffuGZZ57hX//6l9WuMqwBYxdJvIUQQgghKgOloFcvx8i3t/flCbiPj2NbixaOdkpddohatWpx1113ERcXx/Hjx/n++++5/fbbueOOO6w2n3zyCV26dOGtt95i586d5f2pKhVZuVIIIWwkK1cKIcpFdrZjcZzUVEfJQF9fxwOYTZuWeqXKyMhI5s6da73u0KEDhmFgGAatW7cubeQVWmn7bEm8hRDCRpJ4CyE8zcWLF1m4cCGmaTJr1ix+++03a98DDzzAZ599ZmN05UuWjBdCCCGEEG5TrVo1Ro4cyVdffcWxY8eYO3cu48aNo3bt2nTv3t1qt27dOiZNmsTGjRtlXriTJN5CCCGEEOKa+Pv7ExERwdSpUzl69Cj33XeftW/atGm8+eabdO7cmRYtWvDCCy+wdu3aKp2ES+IthBBCCCFKzc/Pj2rVqlmvY2JiePjhh6lfvz579uzhnXfeoVu3blx33XW8/fbbNkZqH0m8hRBCCCFEmevVqxcfffQRqampLFmyhEcffZQGDRpw4MABUlNTrXanT58mKSmJ3NzLF/2pbCTxFkIIIYQQ5cbb25t+/frx/vvvc/jwYZYvX85jjz1m7Y+Li6N37940adKExx9/nGXLlpGTk2NjxOVHEm8hhBBCCOEWXl5e9O7dm5YtW1rblFI0a9aMtLQ03n//ffr160ejRo3405/+xJIlS2yMtuxJ4i2EEEIIIWzzwAMPsG/fPtasWcPzzz9P8+bNOXbsGB9//DFTpkyx2uXm5pKVlWVjpKUnibcQQgghhLCVUoquXbvy9ttvs3v3bjZs2MBLL73EQw89ZLVZvXo1oaGhjB8/nsTERC5dumRjxNemdEsXCSGEEEIIUYaUUnTq1IlOnToV2L5kyRJOnz7N1KlTmTp1KkFBQYwcORLDMBgyZEiBiioVVYUb8VZK1VVKzVBKnVdKHVBK3W13TEIIIX6nlPJXSn3u7KPTlVIblFLD7Y5LCFG5vfjiiyQnJzN58mQ6dOjA2bNnmTZtGlFRUXTo0MEj6oNXuMQb+AC4BIQCY4GPlFJt7Q1JCCFEPj5ACnArEAS8AnyvlAq3MyghROXXpk0bXnnlFX799Vd27NhhLdAzaNAglFIAnDp1ijvvvJPY2FjOnz9vc8QFqYp0daCUCgROA+201jud26YBh7XWLxT1fV27dtVr1651U5RCCFF2lFLrtNZd7Y6jtJRSm4DXtdZxV2srfbYQoqxlZWXh6+sLwNSpUxk/fjwA1atXZ/jw4RiGQWRkJDVr1izV+5S2z65oI94tgZy8pNvpV0BGvIUQooJSSoXi6L+T7Y5FCFE15SXdAIMHD+bvf/87t9xyCxcuXCA+Pp67776bkJAQRo8eTXZ2tm1xVrSHK2sAZwttOwtcdnmilJoATHC+zFRKbSnn2NyhHnDC7iDKiHyWiqeyfA6oXJ/lRrsDKA2llC8wHfhSa739Cu08oc+uqD9XElfJSFwlU2XiyszMZObMmQWS9GtQqj67oiXe54BahbbVAtILN9Rafwp8CqCUWltJbtVWis8B8lkqosryOaDyfRa7YyhMKbUUx/xtV5K01r2d7byAaTiey3msiPaAZ/TZElfJSFwlI3GVTEWOqzTfX9ES752Aj1KqhdZ6l3NbR+T2pRBCuI3Wut/V2ijHU0yf43gQPkJr7dmrWgghhBtUqDneWuvzQDwwWSkVqJTqBUThGFERQghRcXwEtAZu01pfsDsYIYTwBBUq8XZ6BKgOHAP+B/xJa321Ee9Pyz0q96gsnwPks1REleVzgHwWWymlmgEPAZ2AI0qpc86vscU8REX9zBJXyUhcJSNxlUyljKtClRMUQgghhBCisqqII95CCCGEEEJUOpJ4CyGEEEII4QYem3grpeoqpWYopc4rpQ4ope62O6ZrpZR6TCm1VimVqZT6wu54rpVSyl8p9bnz7yNdKbVBKTXc7riulVLqa6VUmlLqN6XUTqXUg3bHVBpKqRZKqYtKqa/tjuVaKaWWOj9D3pziHXbHVBpKqTuVUtuc/dgepVQfu2Mqa9fSLyilnlZKHVFKnVVK/Vcp5V9OsRW771VKjVNK5eT72TunlOpnd1zO9u46X8X+vVue56uEcbjl3JQkLnf+LDnfryQ/5+48X8WKy4bzVaI+q6TnzGMTb+ADHLVjQ4GxwEdKKU9d4TIVeAP4r92BlJIPkIKj/m8Q8ArwvVIq3M6gSuEtIFxrXQsYCbyhlOpic0yl8QGwxu4gysBjWusazi+PXXxGKTUYeAf4A45FwvoCe20NqnyUqF9QSg0FXgAGAuFAc+D1coqtpH3vL/l+9mporZfaHZebz1dJf++W1/kqVhxuPjfFjsvJXT9LUMyfJxvOV0n+/bnzfBW7z7qWc+aRibdSKhCIBl7RWp/TWq8AEoB77Y3s2mit47XWM4GTdsdSGlrr81rr17TW+7XWuVrrOcA+wCOTVa11stY6M++l8+t6G0O6ZkqpO4EzwI82hyJ+9zowWWu90vnv5bDW+rDdQZW1a+gX7gc+d/77Ow1MAcaVU2wVsu8tYVxuOV8V5fduCeNw289SRTk/rpTg58lt56uEcblVCfusEp8zj0y8gZZAjtZ6Z75tvwKeOuJdKSmlQnH8XXnsAkhKqQ+VUhnAdiANSLQ5pBJTStUCJgPP2B1LGXlLKXVCKZVUnrcby5NSyhvoCoQopXYrpQ4ppd5XSlW3O7byVox+oS2O/jzPr0CoUiq4vGMrhs7On72dSqlXlFIVYRE6d52va/m9Wx7nqyRxuPNnqaTnpyr/LF0L287XVfqsEp8zT028awBnC207i+N2ragAlFK+wHTgS631drvjuVZa60dw/Fz1wbG4U+aVv6NCmoLjijzF7kDKwPM4buU1xlFLdbZSyhPvQoQCvoCB42erE9AZmGRjTOWumP1C4f497//t7t9/AtoB9XGMbN4FPGtrRA7uOl8l/b1bXuerJHG482epJHFV9Z+lkrLtfBWjzyrxOfPUxPscUKvQtlpAug2xiEKUUl44Vhu9BDxmczilprXOcd42bAL8ye54SkIp1QkYBPzT5lDKhNZ6ldY6XWudqbX+EkgCIuyO6xrkrfT4b611mtb6BPAPPPCzKMcDr7qIrxX52hW3Xyjcv+f9f4n69+LGVVxa671a633OW8+bcdxFMkp6nLKOC/edrxL93i2r8+VCSeIok3NT1nGV47kpLXeer2Kz63wVs88q8Tnz1MR7J+CjlGqRb1tHPHhKQ2WhlFLA5zhG9KK11lk2h1SWfPC8Od79cDzwcVApdQSYCEQrpdbbGVQZ0oCyO4iScs4FPIQjfo+mte6ntVZFfPWGEvcLyTj68zwdgaNa6xLNAy1OXKV0TT975RCXu85XaX/vltW/1ZLEUSbnphziKqyi9GPuPF+lUe7nqwR9VonPmUcm3lrr8zhu+09WSgUqpXoBUTiuTDyOUspHKVUN8Aa8lVLVKsh8r2vxEdAauE1rfeFqjSsqpVR95Sj1VkMp5e18cvkuYLHdsZXQpzguFjo5vz4G5gJD7Qvp2iilaiulhub9+1CO5cn7Agvsju0aTQUed/6s1QGeAubYG1K5KUm/8BXwgFKqjfO8TAK+KI+gStL3KqWGO+d6opRqhaPSwSy748JN56ukv3fL63yVMA63/SyVJC53/iw536O4P09uO18licvd58upuH1Wyc+Z1tojv4C6wEzgPHAQuNvumErxWV7j96oZeV+v2R3XNXyOZs7YL+K4/ZL3Ndbu2K7hs4QAy3BUAvkN2Az80e64yuBzvQZ8bXccpfg7WYPjFt4ZYCUw2O64SvF5fIEPnZ/lCPAeUM3uuMrhc16xXwDCnK/D8n3Pn4Gjzn97UwH/coqtyL63cFzA35wxncdR9nEy4Gt3XG4+X0X+3nXn+SoqDjvPTUnicufP0pV+nirA+SpWXDacryL7rLI4Z8r5TUIIIYQQQohy5JFTTYQQQgghhPA0kngLIYQQQgjhBpJ4CyGEEEII4QaSeAshhBBCCOEGkngLIYQQQgjhBpJ4CyGEEEII4QaSeAshhBBCCOEGkngLIYQQQgjhBpJ4iypPKbVQKaWVUmMKbVdKqS+c+962Kz4hhBC/kz5beDJZuVJUeUqpjsB6YAfQXmud49z+dxxLwf5Haz3BxhCFEEI4SZ8tPJmMeIsqT2v9KzANaA3cC6CUeglHB/498LB90QkhhMhP+mzhyWTEWwhAKdUE2AUcBf4G/BtYAIzUWl+yMzYhhBAFSZ8tPJWMeAsBaK0PAf8HNMPRgf8MjHHVgSulHlFK7VNKXVRKrVNK9XFvtEIIUbVJny08lSTeQvzueL7/f0BrnVG4gVLqDuBfwF+Bzjg6+3lKqTD3hCiEEMJJ+mzhcWSqiRCAUuouYDqO25YNgI+11n9y0W4VsElr/cd823YBptb6RXfFK4QQVZn02cJTyYi3qPKUUhHAl0Ay0AHYDjyolGpVqJ0f0AVYWOgQC4GebghVCCGqPOmzhSeTxFtUaUqp3oAJHAKGaK2PA68APkDhOrD1AG8cIyz55Y24CCGEKEfSZwtPJ4m3qLKctWDnAGeBwVrrNACttQmsBaKKeAin8Pws5WKbEEKIMiR9tqgMJPEWVZJS6gYcpac0MFRrvadQk7y5f+/m23YCyOHykZL6XD6iIoQQooxIny0qC3m4UogScD6o82v+VdGUUjuBOHlQRwghKhbps0VF42N3AEJ4mH8A05RSq4EkHCukNQI+tjUqIYQQrkifLSoUSbyFKAGt9XdKqWBgEtAQ2AJEaK0P2BuZEEKIwqTPFhWNTDURQgghhBDCDeThSiGEEEIIIdxAEm8hhBBCCCHcQBJvIYQQQggh3EASbyGEEEIIIdxAEm8hhBBCCCHcQBJvIYQQQggh3EASbyGEEEIIIdxAEm8hhBBCCCHcQBJvIYQQQggh3OD/A8+iAyP+gykHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(12, 3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys == 1], Xs[:, 1][ys == 1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys == 0], Xs[:, 1][ys == 0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys == 1], X_scaled[:, 1][ys == 1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys == 0], X_scaled[:, 1][ys == 0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 硬间隔和软间隔分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XLgDmzp3L8OHD6dmzJ/3798fLy8t0W1prtmzZgp+fH7/99ptlf9OmTfH19aVBgwZW3Xdx6dIl8ufPj4ODAwA9evRg+/btDBgwgB49epAtWzbTbQnxokqLMVtmroUQQmRYrq6uloE1wP79+7l9+zZfffUVhQsXpn379uzbt89UW0opGjRowK+//srp06fp3bs3Li4ubNiwgUaNGlG2bFkWLlyY6A3ZcXl6eloG1uHh4Rw4cICAgAB8fX3x8PDggw8+sGoJmxAibZDBtRBCiBfGN998w8GDB+nYsSNKKcs66OrVq/P777+bbqdEiRLMmTOHoKAgJkyYQL58+Thx4gQ9e/a0upIkGMtGjh49yrp166hVqxZ37txhypQplkqS/v7+SblcIUQqkMG1EELEkN5TQIlnq1SpEsuXLycgIIARI0aQM2dO9u3bl6Sqim5ubowcOZKLFy+ybNkyKlasGKuSZI8ePTh+/LiptqIrSe7YsSNeJcmMtIRTCFtKizFbBtdCiDTNmjRLtkjJlFAKKHd3487ztJTqSTy/ggULMnHiRIKCgpg3bx7t27e3vDZixAgGDhzI+fPnTbUVXUny0KFD7Nixg9atW/P48WMWLVpE2bJlLctJzBaSiVlJctGiRZabMrXWtGnThi+++IJbt25Zfc1CJDdr4/Dzxu3E0vZB6qXokxsahUgFoaGhuLi4pHY30gVrblZJrhtbUuqGGbmhMW24e/cuefPm5cGDByilaNWqFb6+vtSsWdOqGxbPnTvH9OnT+eabbyyFY4oXL86gQYPo0qULmTNntrpv+/bto3r16oCRh7979+4MHDiQIkWKWN2WEMnB2niZnuO23NAoRBoQERFB7969yZ07N6dOnUrt7gghEuDq6sqePXvo1q0bjo6OrFu3jtq1a1OpUiW+/fZb04VkoitJBgcH88UXX+Dh4cGZM2fo06eP1ZUko1WpUoVff/2VBg0axKok2bp1a3bs2CHLR4RIA2TmWogUcvfuXVq2bMmBAwd48OAB7u7uHDt2jJdeeim1u5amycx1xpUeYvaVK1eYPXs2s2bNshSO+euvvyhfvrzVbYWHh7N69Wr8/PzYv38/YNzI2L59e3x9fXnllVesau/48eNMnTrVMuDPmTMnwcHBSZoRF8JWZOZaZq6FSBF37tyhYsWK7N27lwcPHgAQEhJCw4YNTaftEkKkvLx58/LJJ59w6dIl5s+fT69evWINrL/88kvOnDljqq3ogXR0Jcl27drFqiRZq1Yt1q1bZ7qQTMxKkmPGjOHDDz+0DKzv37/PpEmTrKokKYSwDZm5FiIF3Lt3jxIlSnD58uVYH9s6OzvTqFEj1q5di52d/K2bEJm5zrjSe8w+dOgQlStXBqBZs2b4+vpSv359q9ZlBwYGMmPGDObPn8+dO3cAKFSoEAMHDqR79+64uromqW9ff/01//vf/3B2dqZLly4MGjSIkiVLJqktIawhM9cycy1EisiaNSvbtm2L94syLCyM33//nQ8//DCVepb2WZNmKblSMqXFVE8i9bm5udGrVy+cnZ357bffaNiwIeXKlWPRokWmP5Hy8vLiyy+/JDg4mGnTpuHj48OFCxcYOHAgHh4eDBkyhMDAQKv7Vr58eZo1a0ZYWBjz5s2jVKlSNGvWjM2bN8u6bJGsrI2XGTJua61TbAP+BxwCHgKLn3JcNyACuBdjq/Os9l955RUtRFq2c+dO7eLiooFYW+bMmfWiRYtSu3silQGHdArG5GdtErPNuXbtmh4/frzOmzev5We6cOHCOjw83Oq2wsPD9Zo1a3TNmjUtbdnb2+u33npL79271+r2Tp8+rXv37h0r7nTq1MnqdoQQ8SUWs1N65voy8CnwjYlj92qts8bYtidv14RIfjVr1mT27Nnxbjh68OABffv2ZefOnanUs+TzvDlMEzrX2i2h97K3T/hYe3vbXn86JzHbhNy5czNq1CguXrzIkiVLKF++PM2bN8c+6j9TWFgYJ06cMNWWvb09bdq0YefOnRw6dIhOnTrFqyS5cuVKwsPDTbWXUCXJ5s2bW16/fPmyVZUkRcaX0jE7sVicruN2QiPu5N4wgvWzZkF2W9tuRpkFERnf8OHDdebMmePNYLu6ump/f3/LcY8ePdIDBw7Uf/zxRyr29vkknN7f2J73fGs2W/crOZDGZq6jN4nZ1omMjNShoaGW5wsXLtSAbtiwof7tt990RESEVe0FBwfrESNG6Jw5c1pihaenp/7yyy/1rVu3rGrr4cOH+vHjx5bnvXr10pkyZdLdu3fXx44ds6otkTGllZidHuJ2YjE7La+5rqCUuq6U8ldKjVZKOaR2h4SwlYkTJ9KwYcN4hWTu3btH3bp1CQkJ4datW9SqVYuZM2fyzTdmJg6FSFUSs6MopXB2drY8v3HjBpkzZ2bz5s00a9aM0qVLM3fuXEvmoGcpUKCApZLkrFmzKFasGJcuXWLIkCEULFjQ6kqSDg7GP43Wmrt37z5XJUkhRAISGnHH3QBnYDiwCTgCHIu5mWkjTnvPmgUpBPhg3HD5MnAK+DCRY3thrAk85OnpmZx/oAhhU6GhobpcuXLa0dEx1uy1o6OjrlChgvb09NSZMmWyzFKlV2llFsTW/UoOpN+Za4nZz3Djxg09efJkXbBgQcvPupubm549e7bVbUVEROiff/5Z16tXz9KWUkq3bt1a79ixQ0dGRlrV3tmzZ3X//v11lixZLO0VL15cb9u2zeq+ifQvrcTs9BC3E4vZZmeuZwEjgIvAOmB1nM2mtNYXtNYBWutIrfVxYBzwZiLHztNaV9JaV8qTJ4+tuyJEsnF2dmbz5s24ubnFSt31+PFjTp8+TXBwsKUS3JUrVyRfrUizJGY/W86cORk2bBgXLlzgu+++o3LlyoSEhJApUybLMWbXUdvZ2fH666+zZcsWjhw5kmyVJHPnzm05xmzubSGE+VR8rYF2WuteWuuxWutPYm7J2L9oGjCfOFSIdCJPnjxs3bqVLFmyxNofFhYW62NZZ2fnDHmzo8iwJGYnwtHRkQ4dOrB//352795Nx44dLa8NGTKEOnXq8NNPP5kezEan/gsMDOTjjz8mT548/Pnnn7zzzjv4+PgwceJEbty4YaqtHDlyMGTIEM6fP8+WLVsoU6aM5bV69erRuXNnDh8+bN0FC/ECMju4fgAEPe+bKaUclFLOgD1gr5RyTmhdnlKqqVLKPepxCWA08NPzvr8QaVHJkiVZu3ZtrDWacd25c4eNGzemYK9sJy3kiE7ovRKr2SO1fJ6QmJ18lFLUqFHD8nMfHh7OqlWr2LFjB61bt6Z48eLMnDmTe/fumWovZiXJBQsWULp0aS5fvszIkSMpWLAgffr0MV1J0tHRkXr16lmenz17lt27d1sqSdauXduqSpIifUnpmJ1YzE3XcTuhtSJxN2AAMBewM3P8U9oZC7GzI0Tt88TIi+oZddyXwFXgPnAB4yNGx2e1n1HvPBcZW1hYmG7Xrl2C2UNibt7e3ineN3f3hNe2ubsnz/vZ2SX8fnZ25vtmzZZc15FUpLE11xKzU9bt27e1n5+f9vb2tny/c+TIoYcOHaovX75sVVuRkZF648aNukmTJrH+/Zo3b65///13q9dlBwQE6MGDB+ts2bJZ2ipUqJCeNm2avn//vlVtieQjMTtlJRazEy1/rpRaH2dXLeA2xo0qj+MM0Fs+ZfyeYtJ7KV3x4gkJCaFx48acOnWK0NDQpx6bKVMm/v33X3LlypVCvUu5st9JeT8rKkw/VXJcR1JJ+XMBxiz2Tz/9hJ+fH3/88QcABw8epFKlpP3XOHXqFNOmTWPp0qWWypFly5Zl0KBBdOzYEScnJ9Nt3b17l2+++YZp06YREBBAtmzZCA4OTnKZdmFbErNTVlLKn4fE2dYCW4ErCbwmhEiCwYMHc+TIkWcOrEHWXQvxonBwcKBt27bs3r2b/fv3M3bs2FgD68GDB7Nq1SrTN0CWKlWKuXPnEhQUxPjx48mbNy/Hjh2jR48eeHp6Mm7cOP777z9Tbbm6ujJw4EDOnj3LmjVr+Pzzzy0D69DQULp3786+ffusv2ghMpKEprPT6yYfMYr05v79+3ru3Lm6UKFCsdJgJbQppXSfPn1StH8pnfbIVimZrNnSEtLYspDk3iRmW+/IkSOWmODl5aW/+uorqwvJhIWF6SVLlujy5ctb2nJyctLvvvuuPn78eJL7NnfuXEt71apV0z/88EOsgjUi+UnMTlmJxWxTS8CVUluVUjkS2J9NKbX1+Yf4QryYMmfOTK9evTh37hwbNmygefPmODs7J/gxrdY63d7UKISwjSJFijBz5kyKFClCYGAgH3zwAR4eHvj6+hIQEGCqDScnJ7p06cKff/7Jtm3baNGiBY8ePWLhwoW8/PLLNGrUiA0bNlhdSKZ58+aMGDGCnDlzsm/fPtq3b0/hwoX58ssvuXXrVhKuVoj0KdE117EOUioSyKu1/i/O/peAf7TWjsnUP6vI+j2REfzzzz/MmDGD2bNnExkZGStbgKOjI1evXiVnzpwp0hdZv5eyZM21MCsyMpJff/0VPz8/tm3bBhhLNq5cuULmzJmtbu/s2bNMmzaNRYsWWSpHlihRgkGDBvHOO+9Y1eb9+/dZunQpU6dOxd/fH4AaNWqwe/duq/slrCMxO2UlZc01SqmKSqmKUU/LRj+P2ipjVNr6Jxn6K8QLq0CBAkyaNIn//vuP2bNnU7p0aTJnzoxSCkdHxxT9BZVWUjIltN8WfUjJdIBC2JKdnR0tWrRg69at/PXXX3Tp0oV3333XMgh+9OgRP/zwA48fP35GS4aiRYsyc+ZMgoODmTx5MgULFuTvv//m/fffx9PTk1GjRvHvv/+aaitLliz06dOH06dP8/PPP1OvXj169epleT0wMJCdO3diZnJPWEdidhqR0FqR6A2IBCKitsgEtvtAj6e1kZKbrN8TGdXBgwd1u3bttIODg+7fv4Xes8dLb9um9J49XvrKlW8TPCc5UzLZau3c82yJpYBKa6marIGsuRbPIWZ6vWXLlmlAFyhQQE+cOFGHhIRY1dajR4/0999/rytXrmxZR+3o6Kjfeecd/eeffz5X3/r166cBXbFiRb1s2TL98OFDq9vLqJIrbqd2vH7RYvaz1lz7AIUxKm1ViXoevRUAsmmtv7HlYF8IEV+lSpVYuXIlR4/OoEGD33n4MBDQPHwYyJkzvbh6dXm8c65eTbitxPanN4ktB80o1yeEtVSMz91dXV0pWbIk//zzDx9++CEeHh7069fPskzjWRwdHXn77bctlSTbtm1LREQEy5Yto2LFitStW5f169ebXpcds28FCxYkd+7cSa4kmZFl5Lj9IsVsU2uu0wtZvycyur17vaMG1rE5OXlRvfrFWPuSc+2drdbOJZf0GtZkzbWwpcjISDZt2oSfnx+bNm0CjEFu//79mTZtmtXtBQQEMGPGDBYsWMDdu3cB4wbLgQMH0q1bN7JmzWq6rdDQUJYvX46fnx+nTp0CwMXFhdmzZ9O1a1er+5ZRJFfclpidPKxec62U6mJ2S96uCyGiPXx4yar9QogXl52dHU2aNGHjxo2cOHGCnj17kilTJkqWLGk55s6dOzx8+NBUez4+PkyZMoXg4GCmTJmCt7c3586do3///nh4eDBs2DCCgoJMteXi4kLPnj05ceIEGzdupEmTJoSGhlK2bFnLMSEhIWSkCUDx4nhahca7cXZlAhwx1lqDMTB/DDzUWmdLth5aQWZBREYnM9fmpNffxzJzLZLbtWvXyJo1Ky4uLgAMGzaMpUuX0rdvX/r06UOePHlMt5VQJUl7e3vatWuHr68vVapUsapvAQEB+Pj4WJ7XqlWLW7du4evrS4cOHXB2draqvfRIZq7TF6tnrrXWrtEb8DZwDKgJOEdtNYEjQMdk6bEQIp5ChSZgZxc7JZadXWYKFZqQSj0SQqQnefLksQystdYcOHCAq1evMmbMGDw8PHjvvfc4efKkqbbiVpLs0KEDACtWrKBq1arUqFHDqkqSMQfW165dw9/fn+PHj9OjRw+8vLz45JNPTFeSFCI1mSoiA3wJDNBa/6G1Do/a/gAGAV8lW++EELG4u3eiePF5ODl5AQonJy+KF5+Hu3unBI5NrI3k7WNKSSwFVEa5PiGSm1KKbdu28fvvv9O8eXMePnzIggULKFOmDI0bN+bPP/803VaVKlX47rvvCAgIYNiwYeTIkYM9e/bQrl07ihQpwpQpU7h9+7bp9vLkyUNgYCCLFy+mXLly/Pfff4wdOxZPT0/effdd02kB05uMHLdfqJidUAqRuBsQCpRNYH85INRMGymxSVqnNGzRIiPnTu3a8V/z8jJe27bN+nbHjDHO7dr1eXonRJqApOITqejvv//Wffr00S4uLhrQf/zxR5Lbunv3rp45c6YuUqSIJZWfq6urHjRokL5w4YJVbUVGRuqtW7fqFi1aaKWUzpIli75582aS+yaErSQWs83OXO8HpiulCkTviHrsB+yz2Ug/LenWzVikVKdOavdEpJY6dYz/A926pXZPUk3evMa3IO5mb5/w/rx5zbfxvMda01+z5wvxIitevDizZs0iKCiIBQsWUL16dctrnTt3ZvTo0Vy5csVUW1mzZqVfv36cOXOG9evXU7duXe7evcvUqVMpUqSIZTmJNrHYVillSf135swZFi1aRI4cOQB4+PAhderUYe7cuZbKki+y9BazbdVGWmN2cP0u4AZcVEpdVEpdBC4CLwHvJU/XRIaSPTsULw6enqndE2GFxPKPWpOv1Jq8rc+b4zUj54gVIqW4ubnx7rvvWnJTnz17luXLl/Ppp5/i6elJ165dOXLkiKm2EqokaW9vz5o1a6hZsyZVqlTh+++/t6qSZLt27SzPV61axY4dOyyVJD/66CMuX75s9TVnFOktZtuqjbTG1OBaa30eKAs0B6ZgzFg3A17WWp9Lvu6JDKNNG/j7b1i6NLV7IoQQwgpFihRh586dtG7dmvDwcJYuXUqFChWoV68eP//8s+lCMuXLl2fJkiUEBgYyatQo3NzcOHToEB07dqRQoUJMnjyZmzdvWtW3t956i++++45KlSoREhLCZ599hre3N126dOGvv/5KyuUK8dzMzlwTtbxkk9Z6utZ6mtZ6szbzeY4QQggh0i2lFDVr1mTt2rWcPXuWAQMGkDVrVrZt20a7du0ICQmxqr18+fIxfvx4goKCmDt3LiVLliQ4OJgRI0ZQsGBBqytJdujQgQMHDrBr1y7eeOMNSyXJzp07m8+THR4OAQHwxx+wbZvxNSDA2C+ElZ5WRGawUso5xuNEt5TrbhoQvQ538WK4cweGDYPChcHFBQoVgo8/hrCwJ8dv2QKNG0Pu3JAlC9SqBbt2Jdz22LFP1vhGRoKfH5QrZ5zn5gYtW8KBA0/v3507RjvlykHWrMZWtiyMGQNPu1N7xw54800oWBAyZTKWcRQtCq1bw9y58T9TunsXxo+HV14BV1fjnPz5oVIlGDoUTpyIffzixebWsF+6BD17gocHODuDjw8MGfL0vj/L7t3w9tvGtTk5Gd/LBg3g++/Tb3JNIYRIBYULF2batGkEBQXx5ZdfMmzYMEtu7PDwcMaPH09wcLCptlxcXOjVqxcnTpxgw4YNNGrUiAcPHjBr1ixKlChBixYt2LZtm+l12a+99hqrV6/m3LlzDBo0iA8//NCytOXixYvMnDmTe/fuxT5Razh+HNavh7/+gsuX4fp14+tffxn7jx+X3xXCOgnd5Rj1HzkAcIvxOLHtQmJtpPRm0zvPu3ZNOLtF7drG/ilTtC5RwnicJYvWjo7GY9C6RQvj2K+/1lopre3stM6W7cnrmTJpvXt3/PeMznzRpYvWb7xhPHZw0Dp79ifn2ttrvWJFwn0+e/ZJ5g3QOnNmY4t+7umptb9//PPmzn1yTPR5WbLE3hca+uT4W7e0LlXqyWt2dlrnzGl8jd43fHjs9zCTLWT+fK3z5DEeZ82qtbPzk/aKFNH68uXEv2eJZQsZNiz2dbi6xu7n229rHRGR8LnR/9YvcCaSmN86s5s1bTzPsdb2Nz1AsoWIdGzFihUa0A4ODrpDhw76wIEDVrdx4sQJ3bNnT+3k5GTJMlKuXDm9ePFiHRYWluS++fr6akDnyJFDDx06VF+6dEnryEitd+3SevVqrVeuTHxbvdo4LjIyye+fUtJbzLZVG6klsZj9tCIyPlrrkBiPE9sKJdfAP0375BPj337XLrh3z9jmzwcHB/j5Z2NWd9AgGDECQkKMmdeLF6F6dXj0CHx9E2/7p5+MbcoUYyb61i04dw4aNoSICOjeHc6fj33Oo0fQti0EBhqzvps2PenX778bNxJeumSsfY5Z6vbBA/jgA+Nxjx7GMffvG+eFhMCGDdChQ+wEldOmwalTkCcP/PKL0d6NG8aMvb8/TJpkzOZba8gQY8Z81y5jZvz+fVi3zpj1P3cOuna1rr1p0+Dzz41+zpoFN28a38/792HlSsiXD1asgMmTre/rCyKx/KPW5Cu1Jm/r8+Z4zcg5YoVI60qUKMFbb72F1prvv/+eKlWqWGaTIyIiTLVRunRp5s+fT1BQEOPGjcPd3Z2jR4/SrVs3vL29GT9+PNeuXbO6b3Xr1uXVV1/l1q1bfPHFF/j4+PB2s2bs373b+L36NBER8N9/8T+RTYPSW8y2VRtpTkIj7rgbYG/muNTeUnTm2sHBmCmOq0ePJ39yde8e//WLF43ZbNA6MDD2a9GzsKD1p5/GPzc0VOvixY3X33039mtLlz7p1/Hj8c89ceLJ7PrChU/279+vLbPv4eHxz0tI06bGOZMmmTtea3Mz187OCX9Pt2598n3ZtSv2a4nNXN+8acx+OzgY15iQvXuNf4ucObV++DD+63XqGG136/asq4vH3T3hv8Ld3a1uKkUk1t+Yk/zp4TrSO2TmWmQAgYGBesiQITp79uyW2ef69esnqa2wsDC9aNEiXbZsWUtbzs7O+r333tMnT560ur39+/frt99+W9vb21vaG922rXbPHppwrMseGnsG+/HjJF1HckgsbkvMTjmJxWyzNzTeVkptVEp9qJSqrpSyT4ZxfvrSrh0UKRJ/f4MGTx5/+GH81728npyX2F/BmTMbs95xOTs/mWVevdr4mYm2apXxtXVrKFMm/rmlSxtrqsGYtY2WLZvx9fFjY6bajOhzbF0h6623Ev6e1q0Lr75qPI6+zmdZvdqYfX/tNahSJeFjqlUz1snfvAmHD8d/PXPm2F+tkN5SC9kifZMQQgB4enryxRdfEBQUxLRp0yhUqBAtW7a0vH79+nUuXrxoqi0nJye6devGkSNH2LJlC6+//jphYWHMnz+f0qVL06RJEzZu3IiO+fvwKaJT/wXs2MGw1q3JnjkzzStW5Opt56gjLgB3LMc/2R8lKMjU+6QEW6S7E8nD7OC6DXAQIxXfduBWzMF2cnUuTXv55YT3v/SS8dXZOeGBIjz5rCOxlEOVKhk3MSakdm3j661bxp3M0aLL1Natm2iXqVcv9rFg3LRYtKixrKR6deMmyr//jj1wj6tZM+Pr9OnwzjvG0pG7dxM/3qyn3ewYfd1my/Hu2WN83b/fyESf2HbpknFcQgEze3bja/QfE0IIIUxzdXVlwIAB+Pv78/7771v2+/n5UbhwYdq1a8fevXtNtaWUsqT++/vvv+nTpw8uLi5s3LiRJk2aUKZMGRYsWEBoaKip9jyAyR07cnnuXKoWLRrjlV5AQcAX47ayGCIijBsdhXgGs3muN2utR2mtXwNyYAy2LwPjgN3J1700LF++hPfbR03qu7sb2TGedkxiSfMLFEh4f9zXYq47i378tHMLFjS+hoQ8GTzb28N33xnnXbgAgwdDyZLGOud27Yw7peMOtLt0gV69jP3ffmsMtnPkgAoVjGwpSZ3RNnPdZtfaRfchNNT4kz2xLfrfIKHKXjK4FkKI52Zvb0+mTJksz+/du4ednR2rVq3i1VdfpVq1aqxYscJ0IZnoSpLBwcFMnDiR/Pnzc+rUKd577z08PT35+OOPn11J8tEjADI7OcXYGQpEAneBqUARoC27//77ycz40/oo6fxEFNN5rpVS7kqp9hhFZL4G3gb+wBhgi5TyrI++Yt6saFalSnD2rDFQ7tLFWCpx44axBKNVK2jePP4NH3PnGstaPv7YmHF2coIjR4wbOYsWhc2bre/H05j8yM8iej2Dr6+5JWkJlTiXwbUQQtjctGnTCAwMZOTIkeTKlYv9+/fToUMHChcuzLp160y3kytXLkaMGMHFixdZvnw5lSpV4vr164wfPx4vLy+6devG0aNHEz45xmD/CRdgK/An0AWwB9ZQ8+OPqTJyJCcuXQJHx/inaS3p/EQspgbXSqmTGAuR3geuAL2BHFrrOlrrT5Kxfy+mp33sFHNWOCq3aKzHgYGJnxude9TNLf6suosLdOoES5YYmUguXDDWjCtlLPuYMyd+e6VLG1lTtm0zlqn8/LOxXOb+fSOzh8lZCAsz1x3zmp8meunNqVPW9SGmSZOMYNivX9LbEEIIEU/+/PmZMGECQUFBzJ49m+LFixMUFESOHDksx4SbnPF1dHSkY8eOlkIybdq04fHjxyxZsoTy5csnXEkyf/4nnyLHUwFYAgQCH+Hm6srJoCDyurkZ58GT7CdaGzPUZ88ak1BxJ6Ki9509axwnA+wXgtmZ6+xABPAAuI/xmcmj5OrUC+/gwYSXKYBR7AWMZRg+Pk/2V6xofN22LfF2t26NfezT+PjAZ59B+/ax3zcxmTLB66/Djz8az//91wgm1njae0S/ZqbvYKwfjz7PyuphtpDeUgvZIn2TEEJYK3PmzLz//vucOnWKrVu3Ujv6/hqgbdu2tG7dmh07dpi6YTG6kMyaNWs4d+4cAwcOtFSSbNmyJSVLlmTWrFncv3/fSFkbg3v2sARazId79lFcmjWLjR99RO5s2cDDg8ePH1OqVCmjkuQvvxhp+lIhnZ8t0t2J5GF2zXVBoCKwFigPrANuKKXWK6WekrA5NqXU/5RSh5RSD5VSi59xrK9S6opS6rZS6hullNPTjs9QHjwwcjTH9fChkfsajMwfMWefozOBbNhgfAwV18mTTzJtvPXWk/2PnvE3kovLk/c2c0708XHPMeOHH4wZ87h27jT+4gdjHbgZ7doZN4WGhRkVI58msRtLn8OVKwmvPnnWMsCY7O2Nf+K4W0KTLdYcmzdv/OOuXjWCb9z+RkQkfB2Q8PsltOXNm7TvoUh9ErNFSrGzs6Nu3bqWiorXr19n06ZN/PTTT9SpU4dXXnmFZcuW8ehZv7OiFCpUiKlTpxIcHMxXX32Fp6cn/v7+9OvXDw8PD0aMGkWwq6slSF6Z/zN65Y/xtivzfyazkxM1y5Qxljw6OLBjxw78/f2NSpKtWtHys8/YduIEdm+1Rb3VLt5m3z7q93P0DHZ4+HPH7Ohf/2aT8YHE7JRkes211vqc1noB0BV4C2OA3RT40or3uwx8CnzztIOUUo2BEUB9wBsoBLw4y0+yZ4fRo40BdvSdzxcuGOufT582MpGMGBH7nPbtjTLnYKTj+/33Jz9RW7YYNx0+fmws5ejU6cl5v/1mzPLOnx97ScmDB8a+5cuN540bP3mtQQMYMMAY9Ma8M/vkySdrl/PlSzyjSmIyZYKmTZ9k+oiMNJaaRP/h0LAh1Khhri03N5g40Xi8aJHxB0XMGYOwMKMser9+ibcZXeo+ofXYKSCxNHgJ7bfmWFukCZQUUC8MidkiVeTOnZuLFy8yZswY8uTJw19//UWXLl3w9vZmwoQJ3Lp1y1Q72bNnZ/DgwZw/f56VK1dSvXp1bt68yeTJk/Fp1oxOc+ZwKGbmrYTY2xuZwKLS3DZo0IDjx4/z7ltvkcnBgZ8PH6beuHFoKmIsJ4m9nCVSx1mGGRQkMTuDM7vmurJSaphSagNwEyMdX0ngK6CZ2TfTWq/RWq8DnvU5fVdgodb6pNb6JjAe6Gb2fdK9Vq2gZUsj13X27JAzp1HxcONG44d80aL4FRAzZTJyO3t5GenlGjaErFmN2dsGDYx9np6wZo1x82FM+/YZ2T+8vY2czrlyGef26mXMUjdrZjyOducOzJhhpMfLmtU43sXFCDzbthltLFtmVKu0xpdfGrPINWqAq6vRdsuWRoaQIkWM9eDW6N/fuMFSKWO5yssvG9+PXLmMrzVrGpUbTaZuEuJFIzFbpCZ3d3fGjh3LpUuXWLhwIWXKlOHff//lk08+MZ1yL5qDgwPt2rVjz5497Nu3j/bt26O15rvNm6k8fDg1x4xhzcGDRMQc3To4GL9zixY1fi/F+LS4TJkyLBgwgEuzZvHJW2/xUvbswFFgAk8dWkk6vxeC2ZnrPzDS7x3FmLXOpbWuprUeobXemAz9Kh31XtGOAu5KKbe4ByqlekV9bHkoKSVR06ToweCUKUZavEePjAH2668bs7pvv53weUWKwNGjRgaPmIVkypQxZsKPHYNixWKfU6+eMRDu2tUYfGbObOSsdnMzBuVLlhizxzEHygsWGDcy1q1rDNijg1yJEvC//xkzxPXrW3/dRYrAoUNGGfbs2Y0g5O1tFM45dCjx9IdPM2qU8T3p1csIkFobN1zmy2fMks+ebeTCFkI8jxc7Zotk5ezsTI8ePTh27BibN2/m888/J1/U74OIiAh69OjB5s2bTReSqVq1KitWrODChQsMGTKE7Nmzs/v0adp+8QVFfX2ZtmMHd3PkgPLljQmel19OOLXuo0e8lD07H7/5JpdmzQIWAZ/xZGgVjJH/Ic6N9dbe7C/SHWXyJoEsWuv7NntTpT4FCmqtuyXy+nmgn9b6/6KeO2LcQOmjtb6YWLuVKlXShw4dslU3U97YscagtWtXWLw4tXsjUlliadIh/g3nyXVsYp7WxvO0+6JTSh3WWldK7X7EJTFbpFVr167ljTfeAIzZ5EGDBtGpUyecnZ2fceYTd+/eZfHixUybNo3z588DkC1bNnr27En//v3x9vZO+MQ//og1C63eintP0AhgMgCNy5XDt3lzGpUrhypQAPVa4kscJWanH4nFbLM3NNpsYG3SPSBmcuHoxzYoAyiEEMLGJGaLVFGrVi0mTJhAvnz5OHHiBD179sTT05OxY8dy1eTiYVdXV/r378+ZM2dYt24dtWvX5s6dO0yZMsVSSXLPnj3xZ8afms4PoDvQB3Bh49GjNPnsM8p88AHzd+7EKFgjMirTNzSmsJNAuRjPywFXtdYpn1NNiFSSWBq8hPZbc6wt0gRKCigRh8RskSrc3NwYOXIkFy9eZNmyZVSoUIFr167xySefUKtWLdNLRcCoJNmqVSu2b9/O4cOH6dy5s6WSZI0aNeJXkoyTzs9OxX2v4sAsFEFM7NiR/Dlzcio4mF4jR2IsF4lPYnbGYOUdZ89HKeUQ9Z72gL1SyhkI11rHzRS/FFislFoO/AuMAhanZF+FSG3PSpua1GOtSQeYnG2ItE9itkgvMmXKROfOnenUqRM7d+7Ez8+P2rVrW1L7/ffffxw8eJCmTZtil9hsRAwVK1Zk2bJlTJ48ma+//po5c+Zw4MABOnTogIeHB/379+e9994jR9GilgIyET+sekqLrRncsiU/BgTgt2oVfn7vUbOm8cqxY8fQWlOuXLkEz5SYnf6k9Mz1KIzPQkYAnaMej1JKeSql7imlPAGi1u19DmzDKJEUCIxJ4b4KIcSLTmK2SFeUUtSuXZt169YxaNAgy/7Zs2fz+uuvU6pUKebMmcODxAq1xRGzkuScOXMslSSHDRtGwYIF6T93LucePXrG8hDA3p5MBQrQafhwDh48yGuvvWZ5acSIEYlXkhTpkqkbGtMLuTlGCJGepdUbGpOLxGyRUubPn8/48eMJCgoCIFeuXPTu3Zt+/fpRoEAB0+1ERkbyf//3f/j5+fH7778DxoC+ZZ06+NatS61SpVBx0/lpbWSrKlMm3p2FkZGRfPDBByxYsIB79+4BUKxYMQYOHEjXrl3JkiXLc165SE6JxexEB9dKqcFmG9daT3mOvtmMBGohRHomg2shks/jx49Zs2YNfn5+7I9Kwerg4MCkSZP44IMPrG7v+PHjTJ06lW+//dZSObJC6dL4tmlD+1q1yJQ5s3HTo4fHM+s+3L59m4ULFzJt2jQuXboEQM6cOVm5ciUNGjSwum8iZSRlcP2MkkUWWmtd6Hk6ZysSqIUQ6ZkMroVIGXv37sXPz4/Vq1ezYcMGGjVqBEBISAg5cuTA/lnLPMLDISgILl/m6pUrzP71V2atX8+1EOMe3nz58tGvXz969+5N7ty5TfcrPDyctWvX4ufnx+HDh7l06RLuUXcYhoSE4OYWL3W8SEVWD67TIwnUQoj0TAbXQqSsoKAgChYsaLnxsW3bthw5coSBAwfSvXt3XF1dY5+gtVEo7exZ43mMu8nDIiL4btcu/DZt4sS5c4BRAKdLly4MGjSIkiVLWtW3gIAAfHx8AGPQXbhwYTw9PfH19aVVq1bP/gNAJLvnynMthBBCCJHReHh4WAbWoaGhHDt2jAsXLjBw4EA8PDwYMmQIgYGBxsFaG4VjorKDxE3T5GxvT486dTg2aRKb/fxo1qwZYWFhzJs3j1KlStGsWTOrKklGD6wBTp8+ze3bt9m9ezdt27alaNGiTJ06lTt37tjmGyFsyvTMtVIqF9AE8AQyxXxNaz3O9l2znsyCCCHSM5m5FiJ1RUREsH79evz8/Ni1axdg5L9+4403mNitG4XDwszlPrW3h6JF+dvRkalTp7J06VJCQ43CMclRSfKTTz4ha9as1l+weC7PtSxEKVUN+BV4COQB/gHyRT2/qLUua9vuJo0EaiFEeiaDayHSjkOHDuHn58fKlSvRWhMwaxYeOXNaXr/qtIsLWb7noV0ITpFuFLrfAfeHNZ80YG8PLVuCgwMhISHMnTuXmTNn8u+//wKQJ08e+vbtS58+fSzrqs2IiIjgl19+YcqUKezcuRMfHx/Onj0ry0RSwfMuC/kCWA4UAMKAehgz2IeAybbqpBBCCCFEWlCpUiWWL19uVH/86is8om5MjIyMpM5nH/DJb7MICb0OSvPQ/jpnXOdy1WlX7EaiUv89rZKkp6cnPXr04Pjx46b6FV1JcseOHRw+fJjZs2dbBtZXrlyhZs2asStJihRndnBdFpipjWnuCMBJa30VGA6MTaa+CSGEEEKkqgIFCtChUiXLcpDfjx9nx5EgZs+N4K23YMYM+OcfiFSPuJDl+ycnRkTA5cux2oquJHn48GG2b99Oq1atePz4MYsWLaJs2bI0bNiQ3377zXQhmYoVK9K4cWPL83nz5rF79246dOhAoUKF+Pzzz7l58+bzfxOEVcwOrh/FeHwV8Ip6fA/Ib9MeCSGEEEKkJY+eDIMavPwyn02AChUgNBTWrIF33oHRo+HA8euxb1hMZPY4ZiVJf39/+vfvT5YsWfj9999p3ry51ZUkow0ZMsRSSTI4OJjhw4dbyrWfi8pgIpKf2cH1n0DlqMfbgU+VUl2B6cCxZOiXEEIIIUTakOlJHgc7OzvqVM3NlCkwfz40aWLUiNm9Gz791I7wmDc8Ojo+s+kiRYowffp0goOD+eKLL/Dw8ODMmTP06dMHDw8PRo4cyT///GOqm5kzZ6Z3796cOnWKX3/9lQYNGnD//n1mzpzJ4MGmawOK52R2cP0REP3ZxijgGjADyAn0ToZ+CSGEEEKkDfnzGzcoRil0vwN2OhNFisDw4bBiBXTtYscHb9bEMaoaY8iDB0xct46QqMIyz5IjRw6GDBnChQsXWLFiBVWrVuXGjRtMnDgRb29vy3ISM+zs7Cyp/44dO0aPHj1iVaE8ePAgS5cutVSWFLYlRWSEECKNkGwhQqRR4eGwfn2sNHxXnXbx+7VlFCh0O8FsIRN/+omRy5fj4uJC165dGTRoEMWLF7fqbWNWkoxeh12rVi18fX1p0aJFkjOEtG7dmp9++inJlSSF4bmyhSiltiqlciSwP5tSaqsN+ieEEEIIkTY5OEDRorFmr4NO5adzz1uonWOofmNWvDR8r9avT+PGjQkNDWXOnDmUKFGC5s2b8/vvv5suJFO9enVWrlzJ+fPnGTx4MNmyZWPnzp20adOG4sWLM336dO7evWv15bRu3ZrSpUvz77//MmrUKDw8POjduzenT5+2ui0Rn9llIXWIUzgmijNQM4H9QgghhBAZR5ky8NJLlgH20GXLYn21sLeHl16idrdu/N///R8nT57kvffew9nZmd9++42GDRvSu7d1K2q9vb356quvCA4OZurUqfj4+HD+/HlLJcmhQ4dy6dIl0+1169aN48ePs2nTJpo2bRqrkuSiRYus6puI76mDa6VURaVUxainZaOfR22VgV4YBWWEEEIIITIupaBGDShalEMBAeyPyr5xKjiYHadOGbPbUZUZqVHDOB4oVaoU8+bN49KlS4wfP568efPSqlUrS7OBgYH8999/prrg6urKwIEDOXv2LGvWrKFmzZrcvn2bL7/8kkKFCtG+fXv27dtn8nKUJfXfqVOn6N27N9mzZ4+V2u/UqVOEhYWZ/Q6JKE9dc62UigSiD1AJHBIK9Ndaf5MMfbOarN8TQqRnsuZaiPShbp06bN+xw/K8cokSHPjtN/DwMAbZT/Hw4UMcHR2xszPmNzt06MDatWvp1KkTvr6+lClTxqq+xKwkGR4eDkC1atUYPHgwbdq0weEZ/YkpNDQUFxcXwKgEWbx4ce7cuUOfPn3o27evVZUkXwRJXXPtAxTGGFhXiXoevRUAsqWVgbUQQgghRHI7dOgQ+w8ciLXvVFAQOy5deubAGsDJyckysI6MjOTRo0c8evSIb775hpdffpmGDRuyYcMG04VkYlaSHDFiBDlz5mTfvn289dZbFClShK+++orbt2+bait6YA1Gtcds2bJx7do1xo0bZ3UlyReZZAsRQog0QmauhUj76taty/bt2+Ptr1y5MgfiDLrNOnv2LNOmTWPRokWWwjElSpRg4cKFvPrqq1a1df/+fZYuXcrUqVPx9/cHIGvWrPTo0YMBAwZQuHBh021prdm5cyd+fn6sX7/eciNm/fr1WbZsGfny5bOqbxnNc2ULiWqgqVLqF6XUKaWUR9S+nkqp+rbsqBBCCCFEWnTo0CH279+f4GsnT55kR4ylItYoWrQoM2fOJCgoiEmTJlGgQAH8/f1jDV6jl3w8S5YsWejTpw+nT5/m559/pl69ety7d4/p06dTtGhR2rRpw65du0xlLIlbSfJ///sfWbJkwd/fnzx58liOi4hZOEeYTsXXCVgJnMVYEhJdcsgeGJY8XRNCCCGESDuGDh1KaGhogq89ePCAoUOHPlf7uXLlYvjw4QQEBLBt2zZ8fHwAYwa5SpUqdOnShb/++stUW3Z2drz++uts2bKFI0eO0K1bNxwdHVm3bh21atWicuXKLF++3HQhmSJFijBjxgyCgoJYtWqVZS33tWvX8PT05MMPPzRdSTKjMztzPQx4T2vtC8T802kfUN7WnRJpz9Wry9m715vt2+3Yu9ebq1eXp3aXhBBCJEJitu09bdY62qlTp5I8ex2To6MjtWrVsjw/evQoR48eZdmyZVSsWJG6deuyfv160+uyy5Urx6JFiwgMDOTjjz8md+7cHD58mM6dO+Pj48PEiRO5ceOGqbZy5sxJlSpVLM/XrVvH5cuXmTRpktWVJDMqs4ProsDeBPbfA7LZrjsiLbp6dTlnzvTi4cNAQPPwYSBnzvSSYC2EEGmQxOzk8bRZ62j3799/7tnrhJQvX55z584xaNAgXF1d2b59O61ataJ48eLMnDnTdLq8vHnz8sknn3Dp0iXmz59PqVKluHz5MiNHjqRgwYL07duXM2fOWNW39957jz179vDmm28SGRnJ8uXLqVSpErVq1WLdunVJuNr0z+zg+jJQLIH9tYDztuuOSIsuXPiIyMgHsfZFRj7gwoWPUqlHQgghEiMx2/YOHTpk+mZFW81ex+Xj44Ofnx9BQUF89dVXeHl5ce7cOT799FOUSihbcuJcXFzo2bMnJ06cYOPGjTRp0oTQ0FBmz55NiRIlLMtJrKkk+eOPP8aqJLlr1y7mzJmTlEtN98wOrucB05VSNaKeeyilugKfA7OTpWcizXj4MOGqT4ntF0IIkXokZtve0KFDLVk8niW5Zq+jZc+encGDB3Pu3Dl+/PFHPv/8c5ycnAC4ffs2Xbt2Nf2HgFKKRo0asWHDBk6ePEmvXr1wdnbm119/pUGDBpQvX57Fixfz8OFDU+1FV5IMCgrCz8+PkSNHWl7bv3+/1ZUk0yvTqfiUUhMAX4yS5wAPgS+11qOTqW9Wk7ROyWPvXu+ojxdjc3Lyonr1iynfISEyKEnFJ2xBYrbt9erViwsXLlieb9myJdbrdnZ21KlTxzKDXKBAAZYsWZKifQT46quvGDJkCACvvvoqvr6+tG7d2qpCMtevX2fOnDl8/fXXXLlyBQB3d3f69u3L+++/z0svvZSkvrVr145Vq1Zhb29P27Zt8fX1pVq1aklqK61ILGZbledaKZUZKIUx431Ka33Pdl18fhKok0f0+r2YHzPa2WWmePF5uLt3SsWeCZGxyOBa2ILE7ORnb28f62ZCOzs7wsLCcHR0fMpZyS8oKIiZM2cyb948bt26BRizyQMGDODdd98lWzbzt8k9fPiQH374AT8/P44cOQIYBXA6d+7MoEGDbFZJ0tfXlzfeeMOqPwDSiiTluVZKZVZKfa2U+kcp9R+wALiotT6QlIG1UiqXUmqtUuq+UipQKdUxkeO6KaUilFL3Ymx1rH0/YRvu7p0oXnweTk5egMLJyUuCtBAvAInZ6ZPE7BeXh4cHkydPJigoiBkzZlCkSBEuXrzI4MGDad++vVVtOTk50aVLF/7880+2bdtGixYtePToEQsXLuTll1+2LCextpJkQEAAw4cPt1SSbN++PRMmTEjK5aZdWutEN+AL4D5Ra66Ba8CPTzvnGe19D/wAZAVeA24DpRM4rhuw29r2X3nlFS1SzpUr3+o9e7z0tm1K79njpa9c+Ta1uyREugYc0kmMr8mxSczOeCRu24adnZ0GLJudnZ1+9OhRancrnvDwcP3TTz/pOnXq6LVr11r2nzlzRu/atUtHRkZa1Z6/v7/u16+fzpw5s+XaS5YsqefOnasfPHhgVVv37t3TX3/9tS5VqpQODAy07N+9e7c+f/68VW2llsRi9lOXhSilzgMfaa1XRD2vAvwBOGutrSrHo5TKAtwEymit/aP2LQP+0VqPiHNsN6Cn1vo1a95DPmJMOfKxoxC2l5aWhUjMzngkbttOWl0W8jRaa8ua8K5du7J06VIqVarE4MGDefPNN63q+82bN5k/fz7Tp0+3FI5xc3Pj/fffp1+/flaVRY/Zr8jISEqVKoW/vz+tW7fG19eX1157zepsKCklqeXPPYBd0U+01gcwisjkT0IfigER0UE6ylGgdCLHV1BKXVdK+SulRiul0t9inAxMUj0JkeFJzM5gJG6/2GIOUIsUKYKbmxuHDh2iY8eOFCpUiMmTJ3Pz5k1TbeXMmZNhw4YREBDAd999R+XKlQkJCWHChAl4eXnRtWtXyzpta/p17949qlevjqOjI2vXro1VSfLx48dWXW9qetbg2h6IWxczHEhK0MyK8ZFiTLcB1wSO3QmUAV4C2gIdgATz2iileimlDimlDl27di0J3RJJIamehMjwJGZnMBK3RbTRo0dz6dIl5s6dS8mSJQkODmbEiBEULFiQH3/80XQ7jo6OdOjQgf3797N7927atm1LREQES5cupUKFClZXksyWLZulkuTo0aNjVZL09vbm5MmTSb3kFPWswbUCvlVKrY/eMFLxzY+zz4yEqjlmA+7GPVBrfUFrHaC1jtRaHwfGAW8m1KjWep7WupLWulKePHlMdkU8LycnT6v2CyHSHYnZGYzEbRFT5syZ6dWrFydOnGDDhg00bNiQ0NBQypcvbznm2rVrpgrJKKWoUaMGq1at4ty5c/j6+iZYSfLePXO5MPLmzcu4ceNiVZLUWlO0aFHLMSEhIVZfc0p51uB6CUZ1xpAY27dAUJx9ZvgDDkqpojH2lQPM/BmiMQb6Io0oVGgCdnaZY+2zs8tMoUIZ7I5fIV5cErMzGInbIiF2dnY0adKETZs2cfHiRcsAVmtNo0aNqFChAkuWLDFdSMbHx4cpU6YQHBzMlClT8Pb25ty5c/Tv3x8PDw+GDRtGUFCQqbZiVpLcs2cPmTJlAuDGjRt4eXlZXUkyxSR0l2NybcAKjLvPswA1SPzO86aAe9TjEsAJYMyz2pc7z1OW3HUuhG2R9rKFSMzOYCRu20Z6yRbyPIKCgvRLL71kuca8efPq8ePH62vXrlnVzuPHj/WqVat0jRo1LG3Z29vrt99+W+/fvz9Jffvtt9+0s7Ozpb2yZcvqRYsW6bCwsCS1l1SJxWyz5c9tpS/gAvyHEbD7aK1PKqU8o/KiRn82VR84ppS6D/wGrAE+S+G+ZhhXry5n715vtm+3Y+9eb65eXZ7osUeONGD7dmXZjhxpYHUbz9sHIUSaITE7FaSFmG2rNkT6VbBgQS5dusSiRYsoW7YsV65cYfTo0Xh4eNCrVy+uX79uqh0HBwfatm3L7t272b9/P2+//TYAK1asoGrVqpblJNGFZcxo2rQply5dYvz48eTNm5djx47RvXt3vLy8GDdunOk13snFqgqNaZ2kdYrPmtRLR4404NatLXGbwMWlFA8fXozVhlKZomasnty9m1i7kv5JCHPSUiq+lCAxO77kitl2dpnJm7crV64sMdW2xO2nS4+p+J6H1pqtW7fi5+fHr7/+ipubG0FBQbi4uCSpPVtXklyxYgV+fn4cPXqUunXrsnXr1iT1y1o2KX+e1kmgjm/vXm8ePgyMt9/JyYvq1S/G2rd9+/MvkUyoXWv6IMSLTAbXInljtj0Qv0SFxG3rvWiD65jOnDmDv78/LVq0AODu3bs0adKE7t2706lTJ6sG3Pfu3WPJkiVMnTqVc+fOAeDq6sq7777LgAED8PHxMd2W1prt27fj4uJCtWrVAKPk+siRI/H19aVx48bY2dl2wUZS81yLdC6lUy8l1K6kfxJCCHOSN14mXPtN4rawRvHixS0Da4Dly5ezZ88e3nvvPTw9Pfn444+5cuWKqbayZs1Kv379OHPmDOvXr6du3brcvXuXqVOnUqRIEd58803++OMP0xlL6tataxlYA8yYMYPNmzfTrFkzypQpw7x58wgNDbX+oq0kg+sMLqVTLyXUrqR/EkIIc5I3XtqbblvitjCrR48efPvtt7zyyitcv36d8ePH4+XlRbdu3Th69KipNuzs7GjRogVbt27lr7/+okuXLtjb27N69Wpee+01qlatyvfff291IRk/Pz8mTZpEgQIFOH36NL1798bDw4NRo0bx77//JuVyTZHBdQZnTeqlHDnqJ9iGi0upeG0olQmI/fFXYu1K+ichhDAnuWK2nV1m8ufvZbptidvCrEyZMtGpUycOHjzIzp07adOmDY8fP2bJkiW8//77VrdXvnx5lixZQmBgIKNGjcLNzY2DBw8mqZJkrly5GD58eIKVJCdPnmx138ySwXUG5+7eieLF5+Hk5AUonJy8Er0hpXz53+MF6xw56lO16sl4bZQo8Q0lSy4y1a41fRBCiBdZcsXs4sXnUazYLNNtS9wW1lJKUbNmTdasWcO5c+cYOHAgI0aMsLx++vRpZs2axf379021ly9fPsaPH09QUFCClST79euHv7+/qbYSqiTZv39/y+v/93//Z1UlyWeRGxqFECKNkBsahUgfXuQbGpPq3Xff5ZtvviFnzpz06tWL//3vfxQsWND0+ZGRkWzatAk/Pz82bdoEGAP6119/HV9fX+rUqYNS1idm0FpTrlw5jh8/TpEiRRg4cCDdunUja9aszzxXbmh8gfn792X7doeoPKgO+Pv3BRLOj2pNXlPJgSqEELaXXDEbJG7bilKKrFmz4urqiqura9qrEJgGtWjRgurVq3Pz5k0mT56Mj48PnTp1wuwf2NGVJDdu3Mjx48d59913yZQpEz///DP16tWjYsWKVlWSjBYeHk737t2fq5JkXDJzncH5+/fl8uXZ8fY7Oubn8ePLCZyhMAoeGSQHqhApR2auRXLFbJC4bUubNm0iJCTE8jx79uw0a9YsFXuUfuzbtw8/Pz9Wr15NRISRwWbSpEkMHz7c6rb+++8/5syZw6xZs7h69SoAefPmpV+/frz//vvkzp3bdFvh4eH89NNP+Pn58ccffwDGJxSbNm2iXr16CZ4jea5fUNu3O5BY+iWzJAeqEClDBtciuWI2SNwWaculS5eYMWMGCxYs4I8//qBUqVKAkUc7X758VheS+f777/Hz8+PYsWMAODs788477zBo0CBL22YdOHAAPz8/du7cyfnz53F2dgbg2LFjlCpVCgcHB0CWhbzAni9Ig+RAFUKIlJM8MTsp+4VITp6ennzxxRf8+++/lsGv1prOnTvj4eHBBx98wMWLF0215eTkRLdu3Thy5Ahbtmzh9ddfJywsjPnz51O6dGmaNGnCpk2bTC/fqVKlCt9//z3nzp2zDKxv375NjRo1KFKkCFOmTOH27duJni+D6wwv4bym1pAcqEIIkVKSJ2YnZb8QKSF68Apw584dMmfOzJ07d5gyZQqFCxemXbt27Nmzx3QhmXr16vHzzz/z999/06dPH1xcXNi4cSONGzfm5ZdfZsGCBaYLycSsNhkQEEDevHkJDAzkgw8+wNvbO9HzZHCdweXP3yvB/Y6O+RM5I/adtpIDVQghUk5yxWyQuC3SvuzZs7Njxw4OHz5M586dsbOzY9WqVdSoUYNq1aqZTr0HRiXJWbNmERwczMSJE8mfPz8nT55MUiVJMPJvnzlzhp9++ok6deokug4bZHCd4RUrNov8+fvwZDbEnvz5+1Cjxj8J5kctWXKZ5EAVQohUklwxGyRui/SjYsWKLFu2jMDAQEaOHEmuXLnw9/cnf/4nf2SGh4ebaitXrlyMGDGCgIAAm1SSbNmyJdu2beO7775L/DhTrYlUZU3qpIRSON28uYMn6/giop7DrVs7Y51769ZOTp9+L+qGF83Dh4GcPv0eAH/8USBWCqg//iiQbNcghBDpWUaI2dZeR1q2adMmmjZtipubG87OzhQrVozhw4ebrvIX19SpU1mzZk28/WPHjo2XZ1kpxdixY5P0PgLy58/PhAkTCAoKYsOGDZbc0/fv36dw4cL079+fc+fOmWrraZUky5cvT/369fnll19MF5JxcnJK9DXJFpLGWZM6KbEUTilF0vYJ8XwkW0j6l5Zjtp1dDuCRqb5llLj92Wef8dFHH9G6dWu6dOlCrly5OHz4MJMnT8bV1ZVt27bh4eFhVZve3t689tprfPvtt7H2jx07lk8++STW2uB9+/ZRsGBBq4qliGdbt24dbdq0AYw/YFq0aIGvry+1a9e2qpDMhQsXmD59OgsXLuTevXsAFCtWjIEDB9K1a1eyZMny1PMlW0g6deHCR7GCG0Bk5AMuXPgo3rGXL89LqW4lKLF+WXMNQgiRnqXlmB0Zect03zJC3N62bRujRo1i0KBBrF27ljZt2lC7dm0GDx7Mvn37uHHjBl26dEnWPlSrVs1mA2tri6NkZK1bt+bo0aN0794dR0dH1q9fT926dXnllVdYtmyZ6dnnQoUKMXXqVIKDg/nyyy/x9PTE39+ffv364eHhwYcffsg///xjdf9kcJ3GWZc66flTOD0vSdsnhHiRpbeYDRk3bn/++efkypWLiRMnxnvNx8eHESNGsH37dvbv38/FixdRSrF48eJYx23fvh2lFNu3bweMWevAwECWL1+OUgqlFN26dUu0DwktCzl69CgtW7YkZ86cuLi4UKNGDXbt2hXrmG7dulGwYEH27t3Lq6++iouLC8OGDUvKtyHDKlu2LN988w2XLl1izJgx5MmTh7/++oupU6daXQY9e/bsfPDBB5w/f56VK1daKklOmjQJb29vqypJggyu0zzrUic9fwqn5yVp+4QQL7L0FrMhY8bt8PBwduzYQcOGDWOleoupZcuWAGzdutV0u2vXriVv3rw0btyYvXv3snfvXkaPHm36/D///JNXX32VGzduMH/+fFavXo2bmxsNGjTg8OHDsY69ffs2b7/9Nh06dGDDhg107NjR9Pu8SNzd3Rk7diyXLl1i4cKFjB8/3jK4PnPmDL179+b06dOm2nJwcLCk/tu3bx/t27dHa813331H5cqVqVmzJmvWrLFUlkyMDK7TOGtSJyWWwimlSNo+IcSLLi3HbDu7HKb7lt7jdkhICKGhoU/NRRz9WlBQkOl2K1SogJOTE7lz56ZatWpUq1aNwoULmz5/6NCheHp6snXrVt58802aNWvG2rVrKVSoEOPHj4917L1795g+fTr9+/enTp06VK1a1fT7vIicnZ3p0aNHrDL0U6dOZd68eZQqVYpmzZqxefNm04VkqlatyooVK7hw4QJDhgwhe/bs7N69m7Zt21K0aFGmTZuW6LkyuE7jrEmdlFgKJxeX2GU/XVxKUaeOBhzjtOAIuMTZ50KdOjpejlVHx/yULPmtpO0TQogY0nLMrlXrpum+pfe4nRaTNYSGhrJjxw7atWuHnZ0d4eHhhIeHo7WmQYMG7NwZOxuMg4MDr7/+eir1NmMYMGAAvXv3xtnZmQ0bNtCoUSPKli3LwoULCQsLM9VGdCXJoKAgpk+fTuHChQkICGDQoEGJnuNgo/6LZOTu3inBO7kvXPiIhw8v4eTkSaFCE3B370SxYrMoVmyWqXbz5+8ZdUNNBEZQ7wkQZ183AGrUSHhBv9lAm9A1CCFERpTWY/aLELdz586Ni4vLU8tnR79mbbaQpLpx4wYRERGMHz8+3ix1tMjISOzsjHnPl156CXv7tLF0KL0qWbIkc+bM4dNPP2Xu3Ll8/fXXnDhxgp49e/Lnn3/y9ddfm27L1dWV/v3707dvX3755Rf8/PzYsWNHgsfK4Dodipsi6eHDQM6cMT5eNBsI46eAikggJdSTfWaDvxBCiNgkZqc8BwcHatWqxebNmwkLC0tw3fX69esBqFevnuX1R48exTomJCTEZn3KkSMHdnZ29OvXL9EsJdEDa8Dqm/JE4nLnzs1HH33E0KFD+eGHH/Dz86NXryfLsg4ePIiTkxNly5Z9Zlv29va0atWKVq1aJfpvJMtC0iFbpEiyJgVUaqf4E0KI9ExiduoYOnQoISEhjBw5Mt5rAQEBTJ48mVq1alG1alXc3d1xcnLixIkTsY779ddf453r5OREaGio1f3JkiULNWvW5OjRo1SsWJFKlSrF20TyypQpE++88w6HDx+mXLlylv2DBg2iXLlyNGjQgF9//dV0Kr/EyMx1OmSbFEnWpIBKG+mihBAiPZKYnTrq16/PuHHj+Pjjj7l48SJdunQhZ86c/Pnnn0yaNIns2bOzbNkywJglbt++PQsXLqRYsWIUL16cX3/91ZKCL6ZSpUqxa9cufvnlF/LmzUvu3LmfeuNkTFOmTKFWrVo0btyYd999l3z58nH9+nX+/PNPIiIimDRpkg2/AyIxMWecHz9+TMWKFTl69Chbtmxhy5YtFC9enIEDB9KlS5dnFpJJiMxcp0O2SZFkzTouWfMlhBBJJTE79YwePZoNGzZw//59unfvTqNGjZg1axZdunTh0KFDeHo++TeYNm0ab7zxBmPHjqV9+/aEhYUxY8aMeG1OnDiR4sWL89Zbb1G5cmWryptXrFiRgwcP4ubmxoABA2jUqBEDBw7k+PHj1KpVyxaXLKzk6OjIjBkzCAoK4vPPP8fDw4MzZ87Qt29fPDw82L17t9VtSvnzdMgWZWmtKbubP3+fF379nhApQcqfZ0wSs4VIPx4/fsyaNWuYMmUKp06dIjg4mOzZswNw7do18uTJYzlWyp9nILZIkZRYCqiE9kmQFkKIpJOYLUT64ejoSPv27dm3bx8nT560DKzDwsIoXbo0tWvXZt26dU8tJJOiM9dKqVzAQqARcB34UGv9XSLH+gLDMZJ4rgb6aK0fPq39F2UWRAiRMaW1mWuJ2UIIYThw4AANGzbkzp07ABQuXJjz58+niZnrr4FHgDvQCZitlCod9yClVGNgBFAf8AYKAZ+kXDeFEEIgMVsIIQCoUqUKQUFB+Pn54ePjg5eXV6LHptjMtVIqC3ATKKO19o/atwz4R2s9Is6x3wEXtdYjo57XB5ZrrfM+7T1kFkQIkZ6lpZlridlCCJGwiIgIrl+/Tt68eVN95roYEBEdpKMcBeLNgkTtOxrnOHellFsy9k8IIcQTErOFECIB9vb2uLu7J/p6Sua5zgrcjrPvNuBq4tjox65ArHJJSqleQHSZnYdKqdgZ4DOW3BjrHjMiubb0KyNfX0pfW+KfM6Y8idnPT3420qeMfG2Qsa8vTcTslBxc3wOyxdmXDbhr4tjox/GO1VrPA+YBKKUOpZWPVJNDRr4+ubb0KyNfX0a+NhMkZj+njHx9cm3pV0a+vrRybSm5LMQfcFBKFY2xrxxwMoFjT0a9FvO4q1rrkASOFUIIYXsSs4UQIglSbHCttb4PrAHGKaWyKKVqAK2AZQkcvhR4VylVSimVExgFLE6pvgohxItOYrYQQiRNSqfi64uRA/U/4HuMPKgnlVKeSql7SilPAK31/wGfA9uAwKhtjIn25yVPt9OMjHx9cm3pV0a+vox8bWZIzH4+Gfn65NrSr4x8fWni2jJU+XMhhBBCCCFSk5Q/F0IIIYQQwkZkcC2EEEIIIYSNZIjBtVIql1JqrVLqvlIqUCnVMbX7ZCtKqf8ppQ4ppR4qpRandn9sSSnlpJRaGPVvdlcp9ZdSqmlq98tWlFLfKqX+VUrdUUr5K6V6pnafbE0pVVQpFaaU+ja1+2JLSqntUdd1L2o7k9p9ykgkZqdPErMzhowYt9NazM4Qg2vga+AR4A50AmYrpRKqIpYeXQY+Bb5J7Y4kAwcgCKgNZAdGAyuVUt6p2Skbmgh4a62zAS2BT5VSr6Ryn2zta+BgancimfxPa501aiue2p3JYCRmp08SszOGjBq300zMTveDa6VUFqAtMFprfU9rvRtYD7yTuj2zDa31Gq31OuJUOcsItNb3tdZjtdYXtdaRWutfgAAgQwQzrfVJrfXD6KdRW+FU7JJNKaXeBm4BW1K5KyIdkZidfknMTv8kbqeMdD+4BooBEVpr/xj7jgIZZRbkhaGUcsf490yoSEW6pJSapZR6APwN/Av8lspdsgmlVDZgHPBBavclGU1USl1XSv2hlKqT2p3JQCRmZxASs9OXFyBup5mYnREG11mB23H23QZcU6EvIomUUo7AcmCJ1vrv1O6PrWit+2L8X6yJUZDj4dPPSDfGAwu11kGp3ZFkMhwoBBTAyJv6s1IqQ81gpSKJ2RmAxOx0KSPH7TQVszPC4PoekC3OvmzA3VToi0gCpZQdRtW3R8D/Urk7Nqe1joj66Lsg0Ce1+/O8lFLlgQaAXyp3Jdlorfdrre9qrR9qrZcAfwDNUrtfGYTE7HROYnb6k9HjdlqL2Q6p9cY25A84KKWKaq3PRu0rRwb6mCojU0opYCHGjU3NtNaPU7lLycmBjLF+rw7gDVwy/vnICtgrpUpprSumYr+SkwZUancig5CYnY5JzE636vBixe1UjdnpfuZaa30f46ObcUqpLEqpGkArjL+q0z2llINSyhmwx/hBcFZKZYQ/iqLNBkoCLbTWoandGVtRSr2klHpbKZVVKWWvlGoMdAC2pnbfbGAexi+c8lHbHOBXoHHqdcl2lFI5lFKNo3/WlFKdgFrAxtTuW0YgMTvdk5idPmXYuJ0WY3a6H1xH6Qu4AP8B3wN9tNYZZRZkFBAKjAA6Rz0elao9shGllBfQG+MH/UqM/JSdUrdnNqExPk4MBm4CXwKDtNY/pWqvbEBr/UBrfSV6w/iYP0xrfS21+2Yjjhip1K4B14H+QGutteS6th2J2emQxOz0K4PH7TQXs5XWOrXeWwghhBBCiAwlo8xcCyGEEEIIkepkcC2EEEIIIYSNyOBaCCGEEEIIG5HBtRBCCCGEEDYig2shhBBCCCFsRAbXQgghhBBC2IgMrsULTyl1USk15Cmvd1NK3UvJPj2NUmqxUuqX1O6HEEKkFonbIi2TwbVIE6ICj47aHiulLiilvlRKZTF5vnfUuZWSu68pJSNekxAi45C4HV9GvCZhvYxUklWkf78D72BUW6oJLACyYFTNEkIIkfZI3BYiDpm5FmnJw6jSrEFa6++A5UBrAGUYppQ6r5QKVUodV0p1jnFuQNTXg1GzBtujzquslNqklLqulLqjlNqtlKr+vB1VSrVQSh1WSoUppQKUUhOUUplivH5RKTVKKTU36n2DlVJD47RRTCm1I6qNM0qpZlGlhLs97ZpinD9QKfWPUuqmUmqRUirz816XEEJYSeK2xG0RhwyuRVoWijEbAvAp8C7QDygFTATmKqWaR71eJeprEyAf8EbUc1dgGcaMShXgCPCbUip3UjullGqM8QtkJlAa6AG8CXwW51Bf4DhQEZgMfB79C0IpZQesBcKBakA3YAzgFOP8xK6JqOspAzQA2gNtgIFJvSYhhLARidsJXxNI3H5xaK1lky3VN2Ax8EuM51WA68APGB8xhgI145wzFfgt6rE3oIFKz3gfBfwLdI6x7yIw5CnndAPuxXi+Exgd55jWwD1AxWjz+zjHnAVGRT1ujBGgC8R4/dWoa+j2tGuK+l4FAQ4x9s0Hfk/tf0fZZJPtxdkkbkvcli3hTdZci7SkSdTd3Q4YMx8/Af0xZjycgf9TSukYxztiBMNEKaVeAsYDdQF3wB5wATyfo5+vAFWUUsNj7LOLajcvxi8BgGNxzrsMvBT1uARwWWv9T4zXDwKRJvtwSmsdHqftqibPFUIIW5G4LXFbxCGDa5GW7AR6AY8xAthjAKWUT9TrLYBLcc55/Iw2l2AEZ1+MgP4Q2AJkeso5z2IHfAL8mMBr157SN82TpVgq6nlSPa1tIYRIKRK3zZO4/YKQwbVISx5orc8lsP8URnD10lpvTeTcR1Ff7ePsfw0YoLX+FUAp5Y6xDu55/AmUSKSvZp0GCiil8mutL0ftq0TsQJvYNQkhRFohcVvitohDBtcizdNa31VKfQl8qZRSGDMlWTFuKInUWs8D/sNY39dYKXURCNNa3wb8gc5Kqf0YawA/50nwS6pxwC9KqUBgJcYavDJAFa31MJNtbAbOAEuUUQjBBZgS1Vb0zEhi1ySEEGmaxG2J2y8y+ThCpBejgbHAEOAkRpBrS1Tao6h1bAOAnhjr2H6KOq8HRkA/DKwAvuEZ6/2eRWu9EWiOsR7wQNQ2gvgffT6tjUiMO8Wdos5fAkzACNBhz7gmIYRIDyRuS9x+IUXfISuESGVKqXIYKacqaa0Pp3J3hBBCPIPEbZEQGVwLkUqUUm2A+xipnrwxPl5UQAUtP5hCCJHmSNwWZsiaayFSjytGkQIP4CawHfCVAC2EEGmWxG3xTDJzLYQQQgghhI3IDY1CCCGEEELYiAyuhRBCCCGEsBEZXAshhBBCCGEjMrgWQgghhBDCRmRwLYQQQgghhI3I4FoIIYQQQggb+X+j6Bg5yTv4zQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "plt.figure(figsize=(12, 2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(Xo1[:, 0][yo1 == 1], Xo1[:, 1][yo1 == 1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1 == 0], Xo1[:, 1][yo1 == 0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\n",
    "    \"Outlier\",\n",
    "    xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "    xytext=(2.5, 1.7),\n",
    "    ha=\"center\",\n",
    "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "    fontsize=16,\n",
    ")\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(Xo2[:, 0][yo2 == 1], Xo2[:, 1][yo2 == 1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2 == 0], Xo2[:, 1][yo2 == 0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\n",
    "    \"Outlier\",\n",
    "    xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "    xytext=(3.2, 0.08),\n",
    "    ha=\"center\",\n",
    "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "    fontsize=16,\n",
    ")\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([(\"scaler\", StandardScaler()),\n",
    "                                   (\"svm_clf\",\n",
    "                                    SVC(kernel=\"rbf\", gamma=gamma, C=C))])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import  train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data\n",
    "def create_data():\n",
    "    iris = load_iris()\n",
    "    df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "    df['label'] = iris.target\n",
    "    df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']\n",
    "    data = np.array(df.iloc[:100, [0, 1, -1]])\n",
    "    for i in range(len(data)):\n",
    "        if data[i,-1] == 0:\n",
    "            data[i,-1] = -1\n",
    "    # print(data)\n",
    "    return data[:,:2], data[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = create_data()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x164f688b670>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:50,0],X[:50,1], label='0')\n",
    "plt.scatter(X[50:,0],X[50:,1], label='1')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SVM:\n",
    "    def __init__(self, max_iter=100, kernel='linear'):\n",
    "        self.max_iter = max_iter\n",
    "        self._kernel = kernel\n",
    "\n",
    "    def init_args(self, features, labels):\n",
    "        self.m, self.n = features.shape\n",
    "        self.X = features\n",
    "        self.Y = labels\n",
    "        self.b = 0.0\n",
    "\n",
    "        # 将Ei保存在一个列表里\n",
    "        self.alpha = np.ones(self.m)\n",
    "        self.E = [self._E(i) for i in range(self.m)]\n",
    "        # 松弛变量\n",
    "        self.C = 1.0\n",
    "\n",
    "    def _KKT(self, i):\n",
    "        y_g = self._g(i) * self.Y[i]\n",
    "        if self.alpha[i] == 0:\n",
    "            return y_g >= 1\n",
    "        elif 0 < self.alpha[i] < self.C:\n",
    "            return y_g == 1\n",
    "        else:\n",
    "            return y_g <= 1\n",
    "\n",
    "    # g(x)预测值，输入xi（X[i]）\n",
    "    def _g(self, i):\n",
    "        r = self.b\n",
    "        for j in range(self.m):\n",
    "            r += self.alpha[j] * self.Y[j] * self.kernel(self.X[i], self.X[j])\n",
    "        return r\n",
    "\n",
    "    # 核函数\n",
    "    def kernel(self, x1, x2):\n",
    "        if self._kernel == 'linear':\n",
    "            return sum([x1[k] * x2[k] for k in range(self.n)])\n",
    "        elif self._kernel == 'poly':\n",
    "            return (sum([x1[k] * x2[k] for k in range(self.n)]) + 1)**2\n",
    "\n",
    "        return 0\n",
    "\n",
    "    # E（x）为g(x)对输入x的预测值和y的差\n",
    "    def _E(self, i):\n",
    "        return self._g(i) - self.Y[i]\n",
    "\n",
    "    def _init_alpha(self):\n",
    "        # 外层循环首先遍历所有满足0<a<C的样本点，检验是否满足KKT\n",
    "        index_list = [i for i in range(self.m) if 0 < self.alpha[i] < self.C]\n",
    "        # 否则遍历整个训练集\n",
    "        non_satisfy_list = [i for i in range(self.m) if i not in index_list]\n",
    "        index_list.extend(non_satisfy_list)\n",
    "\n",
    "        for i in index_list:\n",
    "            if self._KKT(i):\n",
    "                continue\n",
    "\n",
    "            E1 = self.E[i]\n",
    "            # 如果E2是+，选择最小的；如果E2是负的，选择最大的\n",
    "            if E1 >= 0:\n",
    "                j = min(range(self.m), key=lambda x: self.E[x])\n",
    "            else:\n",
    "                j = max(range(self.m), key=lambda x: self.E[x])\n",
    "            return i, j\n",
    "\n",
    "    def _compare(self, _alpha, L, H):\n",
    "        if _alpha > H:\n",
    "            return H\n",
    "        elif _alpha < L:\n",
    "            return L\n",
    "        else:\n",
    "            return _alpha\n",
    "\n",
    "    def fit(self, features, labels):\n",
    "        self.init_args(features, labels)\n",
    "\n",
    "        for t in range(self.max_iter):\n",
    "            # train\n",
    "            i1, i2 = self._init_alpha()\n",
    "\n",
    "            # 边界\n",
    "            if self.Y[i1] == self.Y[i2]:\n",
    "                L = max(0, self.alpha[i1] + self.alpha[i2] - self.C)\n",
    "                H = min(self.C, self.alpha[i1] + self.alpha[i2])\n",
    "            else:\n",
    "                L = max(0, self.alpha[i2] - self.alpha[i1])\n",
    "                H = min(self.C, self.C + self.alpha[i2] - self.alpha[i1])\n",
    "\n",
    "            E1 = self.E[i1]\n",
    "            E2 = self.E[i2]\n",
    "            # eta=K11+K22-2K12\n",
    "            eta = self.kernel(self.X[i1], self.X[i1]) + self.kernel(\n",
    "                self.X[i2],\n",
    "                self.X[i2]) - 2 * self.kernel(self.X[i1], self.X[i2])\n",
    "            if eta <= 0:\n",
    "                # print('eta <= 0')\n",
    "                continue\n",
    "\n",
    "            alpha2_new_unc = self.alpha[i2] + self.Y[i2] * (\n",
    "                E1 - E2) / eta  #此处有修改，根据书上应该是E1 - E2，书上130-131页\n",
    "            alpha2_new = self._compare(alpha2_new_unc, L, H)\n",
    "\n",
    "            alpha1_new = self.alpha[i1] + self.Y[i1] * self.Y[i2] * (\n",
    "                self.alpha[i2] - alpha2_new)\n",
    "\n",
    "            b1_new = -E1 - self.Y[i1] * self.kernel(self.X[i1], self.X[i1]) * (\n",
    "                alpha1_new - self.alpha[i1]) - self.Y[i2] * self.kernel(\n",
    "                    self.X[i2],\n",
    "                    self.X[i1]) * (alpha2_new - self.alpha[i2]) + self.b\n",
    "            b2_new = -E2 - self.Y[i1] * self.kernel(self.X[i1], self.X[i2]) * (\n",
    "                alpha1_new - self.alpha[i1]) - self.Y[i2] * self.kernel(\n",
    "                    self.X[i2],\n",
    "                    self.X[i2]) * (alpha2_new - self.alpha[i2]) + self.b\n",
    "\n",
    "            if 0 < alpha1_new < self.C:\n",
    "                b_new = b1_new\n",
    "            elif 0 < alpha2_new < self.C:\n",
    "                b_new = b2_new\n",
    "            else:\n",
    "                # 选择中点\n",
    "                b_new = (b1_new + b2_new) / 2\n",
    "\n",
    "            # 更新参数\n",
    "            self.alpha[i1] = alpha1_new\n",
    "            self.alpha[i2] = alpha2_new\n",
    "            self.b = b_new\n",
    "\n",
    "            self.E[i1] = self._E(i1)\n",
    "            self.E[i2] = self._E(i2)\n",
    "        return 'train done!'\n",
    "\n",
    "    def predict(self, data):\n",
    "        r = self.b\n",
    "        for i in range(self.m):\n",
    "            r += self.alpha[i] * self.Y[i] * self.kernel(data, self.X[i])\n",
    "\n",
    "        return 1 if r > 0 else -1\n",
    "\n",
    "    def score(self, X_test, y_test):\n",
    "        right_count = 0\n",
    "        for i in range(len(X_test)):\n",
    "            result = self.predict(X_test[i])\n",
    "            if result == y_test[i]:\n",
    "                right_count += 1\n",
    "        return right_count / len(X_test)\n",
    "\n",
    "    def _weight(self):\n",
    "        # linear model\n",
    "        yx = self.Y.reshape(-1, 1) * self.X\n",
    "        self.w = np.dot(yx.T, self.alpha)\n",
    "        return self.w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'train done!'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVM(max_iter=100)\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考\n",
    "[1] Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014.https://www.coursera.org/course/ml\n",
    "\n",
    "[2] 李航. 统计学习方法[M]. 北京: 清华大学出版社,2019."
   ]
  }
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